How Backend Data Shapes Front-End Growth with Sarathkumar Nallusamy

Speaker A: Hello everyone and welcome back to the Marketing Roundtable, a digital marketing podcast. My name is Brandon Jones. I'm the producer along with a paid media strategist. And I'm going to kick it over to Brian Cosgrove, the host for today.

Speaker B: Hi, I'm Brian Cosgrove. I'm an analytics strategist and principal here at Braindew Consulting. And I'm going to kick it over to Sarath Kumar.

Speaker C: Hi all, I'm Sarath Kumar. I'm a supply chain data scientist and an independent consultant for retailer and e commerce companies. And also I'm working on, I'm a, I'm a startup co founder for a green logistics company.

Speaker A: Glad to have you. Welcome on.

Speaker C: Thank you so much, Brandon and Brian. I'm excited to be on the podcast.

Speaker B: Awesome. Yeah, we're excited to have you. Uh, we really appreciated you, you know, meeting you at uh, our analytics events here in Philadelphia. Um, and I think you have an interesting perspective. Your career has taken definitely some interesting twists and turns. Tell us a bit about how you got into analytics. Tell me just sort of, um, you know, early on you're in high school. Tell me a bit about what got you inspired to go in this direction.

Speaker C: M. Yeah, sure, Brian. So right from my school time, I'm good at mathematics and physics as one of my favorite subjects. And uh, I love to think logically and sometimes I look up some numbers for my facts with my friends and whatnot. Right. So that's kind of where it all started. And back to bachelor's day to my industrial engineering. I did my capstone project which was literally, uh, a supplier selection for a chemical manufacturing plant. So there we did a bunch of data models. What we had to do is you have set of hundred suppliers, you got to select, pick and choose the best one and you have to come up with the best strategy of how we can procure at any time. Right. Be it as a peak demand or non peak demand. So we put up like multiple models and then we came up with some certain qualitative and quantitative answers and then we figured out, okay, top five suppliers, ah, are the ones you have to go. And this is exactly how much you need to order. Right. So we did this project closely working with the consulting company and that's exactly how it started. Right.

Speaker B: Okay, so I just want to pause here. So you decided you want to get into this. You decided data science is your thing. How'd you even find out that data science was a thing?

Speaker C: You know, I don't really do know. Right. Because it's logical, it's driven by numbers. Uh, that's exactly how my thinking process is. And I really don't know. I mean data science is the word I got to know after coming to us, but uh. Right, exactly. It's math, right? It's all math. Statistics, distributions. Right. Uh, uh, back in my bachelor's, my industrial engineering courses had different courses like operations research, supply chain analytics by itself. And uh, how do you improve a process through numbers, like a quality control. Out of 1 million products, how do you pick and choose which product to test? Right. You cannot test all the products. So there we have like six sigma quality, uh, concepts, uh, all coming in. And then we have like operations research courses which touched like routing. Like how is the last mile routing would work. Right. I literally worked out the math by hand and then I figured out okay, well this, this is a great work. This is so interesting and like um, and it's going to keep me on toes and challenging. So. Yeah, that's exactly how I got into it. And after getting into. I got no, yeah, it's a data science. Okay, yeah, I'm uh, happy to call myself data science.

Speaker B: I got you, I got you. So you were into math, you decided to go and start your career. You decided industrial engineering. Can you tell us a little bit about how you came to industrial engineering? Of all the kinds of different math projects, you know, majors you could have gone in.

Speaker C: Yeah, sure. So as you know, uh, I was, I know I'm going to be an engineer, not a doctor or anyone. And and uh, I got into and, and in front like I wanted to do a mechanical engineering at first because I'm, I'm super passionate about automobiles. Uh, um, even day in and day out I have my simulator that I would be riding it and, and enjoying it and I cars to my friends and whatnot. Right. And even today I can like dismantle a complete, an engine, a car engine ground up and I can like rebuild it back so I know each and

Speaker B: every car, what kind of cars.

Speaker C: Yeah. So I mean I drive a dot Charger.

Speaker B: That's Charger.

Speaker C: I do, yeah. I love, I ride that right now because it's a, I love American cars. I mean American muscle cars is one of my uh, dream faster drive. So yeah, because I'm here, I'm zoning it. So yeah, I love, I mean I crazily watch F1 race. Formula 1 race is my go to thing and uh, every Sunday I'm locked up. I do not worry about anything else. My wife, my son. Now I don't give a damn about them. But, yeah, I'm super passionate about this automobile science and I really want to get into it. But then when I got into there, I started feeling like, okay, uh, I got to be on a shop floor and do these things and working with all the metals and what not. Like, then I started, like, uh, re questioning myself. Okay, so, like, what I want, really want to do, right? Then I got an option of, like, saying, okay, industrial engineering. Okay, what is that? I'd never heard of the name. Uh, okay, let me get into. Look into it, right? And then I figured out it's, uh, a management science and it's a vast, huge lot. Uh, more things are, like, happening in the world, right? So whenever I type industrial engineering, it shows, like, yeah, uh, uh, there's, like, um, industrial engineers doing these things in, like, China or like, in these things in United States. Uh, um, German, uh, engineers doing this. Right? So I started, like, connecting the dots to see. Okay, this is actually a really big deal, right? So let me go and explore, right? Because I want to see, okay, what's in there for me and when. The moment I got into it very first semester, I figured out, yeah, this is what I want to do, right? It's got data, it's got, like, the management, size analytics. Um, it's got all parts, like, to keep me on to. Right? So that's exactly how I got into there. And.

Speaker B: All right, super interesting. Um, so you saw it as being a lot broader. You could work on bigger problems. Lots of different industries kind of fall into it. It's not completely narrow. And, um, that excited you. So let's get to. You said you had a really amazing internship there. Tell me a bit about that.

Speaker C: Sure. So the internship out there, which is my first data analytics project, uh, uh, is for a chemical manufacturing plant based out of Chennai. And, uh, they were looking out for a specific product that they're trying to source, and it's actually procurement analytics. So they wanted to understand which supplier I can get this product from. Right? Um, this specific product. Then they wanted to. Okay, so that's exactly the problem statement. And we are closely working with, uh, another, uh, consulting firm who actually had this project with because obviously it's a huge project, right? And they already been a client with them. And we are associated with this consulting team and working closely with my professor team, we figured out. Okay, hey. Okay, we need to figure out this supplier, and we have thousands of suppliers. How are you going to select it? Right? Who's the best one? Who's Going to have uh, give us this product at the asked quantity. Right. Uh, through peak and non peak times. Uh, how we can trust. Right. It's out of nowhere. Right. And the problem multifolds. Uh, there are suppliers both from domestic and international. Right. So both mostly from China, one from Europe and the other from Vietnam and whatnot. Right. So putting this all together, there were like hundreds of suppliers. We came up with a list and, and, and uh, now we need to know, okay. Who to select. Right. Okay. Then, then we went on to talk to experts inside the company by themselves saying okay, hey, this is what matters to me. This is what matters to me. We literally spoke to like broke down and spoke to 10 teams to understand what's your priority. Right. Um, we spoke with the like different procrastinating procurement professionals inside the company and we figured out to say hey, we created um, uh, a ah, survey metric because uh, we also call it like, uh, it's called analytical hierarchical processing. We go by the hierarchy and we rank based on the experts saying okay, yeah, these are the things would do good. And out of it it came up with what is the minimum order quantity. Right. And uh, what is the lead time I'm looking at? Right. Because if I'm sourcing from domestic, I can do it in a week, but if I'm doing it in a international way, then I've got to wait for like a month or at least I want to have.

Speaker B: Right.

Speaker C: Exactly the same dynamics directing the US supply chains. I exactly saw that the problem right there, uh, when I was like 19, age of 19.

Speaker B: Okay, so you're solving major pieces there. This is an interesting dynamic. So a consulting company that works with a chemical engineering company is saying, hey, we want to buy some materials. Who do we buy them from?

Speaker C: Mhm.

Speaker B: What's the best decision we could be making here?

Speaker C: Uh-huh.

Speaker B: And as you're working with them as a 19 year old, you're working with consultants who are, you know, having your team do a, do a lot of the support, you know, behind the consulting. Um, you were doing intake, you were gathering the requirements, the preferences, the needs to create effectively a model of what success is, what looks good, what we value, what's important. And you're classifying and you're grouping all of these other entities, anybody you could buy these materials from, putting them into a list, calculating out, these are the ones you should go with. You did this at 19 and as you just described, it's important today. These are important business decisions to do even now. So you have this amazing Experience. You're doing industrial engineering, you're using math that you were passionate about. Um, help me understand how you get from this key experience. I know you had mentioned before when we were talking earlier about you had this opportunity with IBM. Just tell me a little bit about kind of that period of your life and you know, where you went from this amazing undergrad experience to you know, this IBM. Mhm experience.

Speaker C: Yes, sure. So um, because of this, this project was going on in my final year of my bachelor's and I was looking for the opportunities and trying to land and that time we have this uh, because ours is a premium institute, a college of engineering, indy. Um, we had like multiple uh, companies that come and like hire directly right on the campus. That's how I got into there. Right. So I was like setting up like to get some mock interviews and then, but then I thought, but then I got into an interview and uh, because the interview was just like this, like just talking back and forth and they were picking my brain on like how is my process thinking? How's the thinking process behind any problem? Right? So how do you approach this? How do you do this? That's exactly what they were looking for, my attitude towards it. And they were like yeah, they're happy with my profile. I was like okay, yeah, that's great. But one thing is they did not really know exactly where I'm going to be working. So uh, that was a problem of that time. Right. So they usually do some hiring of like 100, 200 people and, and they do an intake based on the project they have and the allocate based on the project. Right, right. And as a fresher out of college, it's like first job, right. And I was like happy because it's IBM, it's a, it's a global brand. And, and, and yeah I, I was uh, earning a decent buck. So I was like, which is like out of for a student. That's like a lot. And, and, and I moved to Bangalore. Bangalore is also an amazing place. Um, uh, where um, a lot more things had just happened there. And I was like okay, yeah, let me give an experience to say ok. Right.

Speaker B: It's amazing.

Speaker C: Exactly a tech hub there. Right. And yeah, that's exactly how what the transition happened there. Um, in a moment IBM.

Speaker B: Okay, so you moved to Bangalore and you're working for IBM. But uh, it's not decision science. So the lesson here is, okay, you go into, you know, as you went into your undergrad you figured out hey, which kind of engineering do you want to do. You do industrial engineering and you get this amazing internship.

Speaker C: Uh-huh.

Speaker B: You're confident, you're feeling pretty good about what you've done so far. When that job fair comes around, you impress these folks at IBM. Uh, you know, because you know that you did something important as a 19 year old.

Speaker C: Yep, yep.

Speaker B: You. But the one downside is they're just looking at you as. Seems smart has a degree in engineering. We'll figure out where, where to place. But you didn't actually get placed intentionally in industrial engineering or where you wanted to be. So you have this kind of period here where you're sort of navigating where you want to be. Tell um, tell me a little bit about, you know, that period and how that bridge to your next chapter.

Speaker C: Sure, sure. So I was like earlier in the times, uh, I know I didn't get into the right project of the IBM and I was, I was placed in a different, like a lead generation software that I got at testing. So. And I wanted to understand, I want to keep my mind open because this is a, maybe this is what the jobs uh, is like about to offer. Maybe my passion is something. Okay, uh, we'll see where it goes. Right. So I was keeping my mind open. I was already learning a bit and learning the new job, picking up, um, uh, what we do is like uh, we have requirements and I write up the test cases with still like confirming the requirements and obviously testing uh, up at the back end and marking it. Done. Right. So. And later we moved on to automation testing a bit on the automation too. Automatically come up with these test cases and like automatically check out the boxes and like, yeah, we're done. Right.

Speaker B: So test cases and.

Speaker C: Exactly, exactly.

Speaker B: And QA work.

Speaker C: Exactly.

Speaker B: And you are working in lead generation. And so some things that, you know, some of our audience here heavily involved in marketing.

Speaker A: Uh-huh.

Speaker B: They might deal with similar kinds of problems. And you're bringing some of your data science background to it, but it doesn't quite match the supply chain global decisioning.

Speaker C: Mhm.

Speaker B: That you were drawn to.

Speaker C: Not yet,

Speaker B: but you met some interesting folks here because now you're working at IBM, global company, big brand, tons of different businesses that they're involved in. Um, there's potentially some internal, you know, I'm sure there's, you were looking at some internal transition, you know, pieces there. But you know, tell me a little bit about your next chapter.

Speaker C: Uh, yeah, sure. So being there for um, I spent like a couple of years being there and, and I figured out, okay, this is not my career path. Right. I got the hunch, strong hunch, saying now, ah, Sarath, you don't belong here. Right? So. And luckily, um, yeah, thank God for it. So it all happened because of them. I joined IBM and I was working for like a couple of years and I was doing like all the repeated projects, but in a different, um, in a different, different projects, but it's all in the same qa, engineering and like testing. That's exactly what my profile was. And I figured out, okay, that's, that's exactly not where I'm going to spend my, uh, uh, spend my thing. But, but let me say this. It's actually a comfort zone, right? Because you get, you're earning good, you're in a Bangalore and you are like, you know, um, you done like single. Like, that's like the best anyone, like, of that age would love to have, right? And I say, like, most of my friends, they just stayed up there, I mean, because it's a comfort zone. And, uh, we have been taught like, right from our childhood, we thought, okay, do not come out of this right, because it's safe being there.

Speaker B: Well, for a lot of people, they're like, I made it, I made it.

Speaker C: Exactly, exactly. But exactly. It hit me because I'm a guy, I always look for next things and I want to do interesting things in my life, right? So that kind of kept me up. And luckily I met a couple of mentors. Um, one mentor who's been, uh, who did the exact, who has the exact industrial engineering background, but he joined IBM same like me. Um, wherever the opportunity was that he just went up there, uh, and then he worked there for like 20 years. Because as I said, it's a comfortable life. Uh, if you want to grow up a family, it's a perfect job, right? And beautiful workplace. Um, you get perfect work, life balance. All things are like checkbox. There is a now thing that you want to break up. Something like that. But, but as I said, uh, I didn't feel enough, right? So I had like the thirst to go explore more, travel more. It's always been there, right from my side, I always had that. So it's like, okay. Then I was exploring the opportunities and I was connecting two mentors. So one mentor is the one who has already came to us, did his master's, worked here, and he got back because of his personal issues. But then he was working on the core data science and AI side. And he has multiple patterns to his place, to his name too. So he's super smart. The Best canon, best, um, one of the smart guy I've ever met. The other guy is like, he's been there for 20 years, but he clearly saw me and he's like, you know, you should go to us. You know, you pursue us, you do masters there. You know, because he has this feeling. See, the mentors are, who are nothing but some of them have made mistakes and they don't want their uh, mentees to do it. Right. Uh, uh, or also they have seen better opportunities elsewhere. Right. And they want the mentees to explore around. Right. Maybe they were feeling so comfortable they didn't explore during that time and maybe their family situation or whatever they relate. But he was like, go for it. And you wouldn't believe this. I'm not sure if this. So IBM, as an IBM employee, uh, I'm just going off mic. But, uh, uh, you can't give the letter of recommendation recommendations because I think uh, there are some company policies. But uh, he still did that and he's like, he badly. What? Like he's like an adopted father and like adopted son. Like he saw me like that and he's like, you should get the, you should have the best things, right? So all those kind of motivations and my burning desire to, you know, really travel and like, you know, see different things and different problems. I want to work on a bigger scale. That kind of pushed me to do, okay, go for masters. Because once you start working, you don't feel like going studying. Right. It's a lot more mental strength you need Going to a country where you don't have friends, you're not a native language speaker and you don't know what's going to happen. It's a 50. 50, right?

Speaker B: You had a great job.

Speaker C: Exactly.

Speaker B: You're an amazing job at IBM. Um, comfortable, obviously people there that really think, uh, a lot of you, you know, those mentors really care about you. But. And you said, hey, I'm going to go back to school, but across the world.

Speaker C: Yes.

Speaker B: And uh, and kind of, you know, and restart with a place where, you know, like you said, you're completely out of your comfort zone. You don't have any friends there, you don't have any contacts there.

Speaker C: Yeah, yeah.

Speaker B: So, okay, so you go and you make this trip. You're now going to grad school in uh, where. Tell me, tell the audience where you go.

Speaker C: When I did go to uh, University at Buffalo. Yeah. It is exactly 20, 2016, January, I got the admit to University at Buffalo. And remember guys, so this is like I got to Write letter of recommendation, statement of purpose, all without AI. Okay. It's pre A.

Speaker B: Yes.

Speaker C: Uh, as a non native English speaker has to convince the admission board of uh, uh, an American university, say hey, yeah, I'm a guy of worth to do this master's, right? So that was a pretty uh, thanks to my friends, the mentors, everyone helped me uh, doing this. That's exactly what is collective knowledge put together in a single document. Like I see the statement of purpose, it's a single page. But trust, uh, me, like 10 people have proofread it. Uh, they have reviewed it and they made it right. And helped me out throughout the way. All those blessings helped me to land up this course. I went to university at Buffalo, New York. And um, yeah, I chose master's in industrial engineering with more on operations research concentration and on all of my courses were in like or and data science. That's where I pick and chose my projects. Uh, during my first semester, uh, I got like really, um, ah, a lot more introduced to biostatistics. The professor and she was working closely with the New York City police. Like the name of the uh, it's actually school, the university police. And we were trying to understand because data science is kind of more of a theory, a lot of theory and a lot more mathematics going there. But to see it in a reality you need to apply the real problems. So we went to an. We were a bit ambitious and we went. And we were like, okay, we were a small group, like three people and we were like. And the professor was like yeah, pick up this project and see, okay, what you guys can do. And literally uh, you wouldn't believe like we had like the uh, criminal database and not the database, but we had the PDFs being shared by the police and obviously the names were redacted and whatnot. But it's basic. We ran a prediction model to say okay, where exactly next crime could happen. And the crime, it's not like a sort of a bigger crime, but it's a really a small time like a theft or a larceny, something like that. Right. So uh, uh, and when do things happen, right, so that they can allocate their police force on that time on that day. So there's exactly. It's more of like a uh, scheduling and planning problem and they want us to predict exactly when it's happened. And we figured out some interesting finds out of it. And uh, yeah, so they needed more people. What do you figure out? They needed more people on Thursdays, Thursday evenings.

Speaker B: Okay, Thursday evenings.

Speaker C: Because that's when bachelor's, uh, uh, bachelor, uh, reservations was the largest group there and they were doing a party and you wouldn't believe there's a party of like 200 people in one house. And they create all the ruckus there.

Speaker B: So that. That's super interesting. So as your industrial engineering degree.

Speaker C: Uh-huh.

Speaker B: They were able to bring your team together with the New York State Police, uh, and now you're applying it for municipal, you know, like use cases. Not just industry, not just in industry, but now you're dealing with municipal challenges and, um, around crime. So it kind of gives people a sense of how broad the applications of this are. Because you're just trying to figure out the right decisions and optimizations and you're figuring out police schedules effectively, right?

Speaker C: Yes, yes.

Speaker B: Having the right people at the right place at the right time. Mm hm. Um, okay, so this is your grad school experience and you're doing this as an intern. And you and three other people are presenting this to New York State Police. Um, tell me, you know, a bit more about kind of how that experience shaped, you know, what the chapter that came next.

Speaker C: Yeah, sure. So this, uh, this work on like the data project with my professor, it kind of definitely opened up, boosted up my confidence, saying, hey, okay, you're not alone. Like in this country you have people who are supporting, supportive of like whatever you want to do. Right. So you're passionate about the data, uh, uh, professor there to guide you. Right. Uh, they, uh, can recommend you. And also the core thing is like the teaching. Right. Uh, it was so good. Uh, I was able to grasp the fundamentals pretty on pretty quick. And obviously we did a bunch of group study, like friends were helping out. That was a completely shared knowledge environment. So which I totally loved it over here, uh, because that is quite different than the Indian system of education. And coming from there, this is quite different. And total understanding was great. And kudos to a lot of PhDs, um, uh, who are doing the teaching assistants. And they helped us a lot understand graphic concepts quick. So with all those things, uh, by the end of the first year, I wanted to do an internship, like a summer internship, so that when I reached out to, I was like any other student doing online checkups and also doing, uh, LinkedIn in a cold text sending out. So I was doing both to see increase my profitability. Uh, I'm sorry, probability. Um, luckily I got my internship at Westrock and this. How this happened is I pinged my LinkedIn contact like two months back and he was Working for a different company. But then he moved to this new company who was looking for like the new internship. So they were growing the team and they were like, okay, yeah, so it's like I, that's a pure uh, I would say a lot of luck in there to get that. And surprise thing was that guy didn't use LinkedIn all this while and he just used it like maybe after years. And somehow he replied to me with a helping, helping attitude. Which is, which is where everything started. And my kicked off my professional journey

Speaker B: there at Westrock, at ah, West Rock. Okay, so super interesting. There's kind of a couple common threads here. Uh-huh. Both those internships that you had, both the one that you had, um, you know, with the chemical company.

Speaker C: Uh-huh.

Speaker B: And this one that you had with the state police. They both had a really big impact on your confidence level. They also both kind of put you in the right position where you were ready to go and again talk to people and convince some people to give you opportunities, um, right from the get go. And they both kind of led to again even great support academically and for mentors and teachers. Uh, so here you are, you're at West Rock. Now a lot of people don't know what that is, but tell us little bit about, okay, you're now a professional, you're in the us you're doing decision science. It's not police stuff anymore. It's not exactly the same as the chemical project that you're working on, but there is some overlap. Just tell everybody a little bit about industrial engineering and the data sets and kind of what you were building there.

Speaker C: Mhm, yeah, sure. So let me share about the best Rock experience. Um, so I was hired to, um, I was hired as a data science intern there. And uh, what we initially did was like the time series forecasting, to be specific, because forecasting also kind of falls under uh, data, pure data science, uh, problem there. And let me tell this. So my background is in decision science, but decision science do span all sort of data science problems. So uh, uh, so I was hired to perform this predictions problem. And to be specific, I was trying to predict the folding cartons. I mean vistroc. As a company, what they do is actually uh. Paper products.

Speaker B: Paper products.

Speaker C: Paper products, exactly. And they literally, well, they own the entire supply chain. I mean the supply chain is huge. Um, they own some to the forests and uh, they do very innovative. We can't call it a deforestation, but they have different uh, uh, regions that they cut in cycles and Each cycle is almost 10 years. So you have to nurture a tree for eight to 10 years and they get that perfect state for a paper cutting. So it's like they have engineered that whole piece. And, uh, you wouldn't believe there is a role name called paper physicist inside the company who literally is to work on the paper. Uh, what is the paper engineering? So much goes into the paper, uh, like your Starbucks cups. Uh, uh, a, uh, paper can hold water only if it's coming from a virgin. So a virgin paper. What is virgin paper is the one which is cut from the trees and it's made, uh, if it is a recycle, then it's like recycle. Right. So they call it like virgin and recycle. So all these sort of like, you have different products, uh, coming in, but then towards the end you are making a paper product and which is like. Exactly. I wanted to. The team wanted me to work on the prediction to say, hey, how much exactly we would need so that we can do a capacity planning for the next year. Right. Going into it for the next quarter, how much I should have the capacity and how much. So I predicted saying, uh, close to 80% accuracy. I predicted their folding cartons, how much exactly the product they would need. And they used that model and they backtracked it to say, hey, this is how much we would need at the back, uh, for making a virgin paper as well as, like the recycled paper. And what would be the mix that should go in? Right.

Speaker B: Uh, so you're dealing with all of their mixes between recycling and, you know, new tree paper and things like that. So you're solving things that are pretty in depth in their industry, in the paper industry particular. Um, and so what's interesting about this is how you're jumping into industries and you have to kind of become an expert in that industry.

Speaker C: Yes.

Speaker B: You know, it's like you have to learn all the lingo and all the terminology and how paper works, things like that, so that you can actually make. Help them make good, informed decisions, you know, And I'm. I'm curious. Like, you know, to me, I feel like a lot of people get stuck in a lane, they learn one industry and they have a hard time transferring their skills to a different industry.

Speaker C: Uh-huh.

Speaker B: How do you approach that challenge where it's like. Because it can be intimidating to have to learn a whole new industry and all of its nuance.

Speaker C: Yes, yes. So this, this is exactly what I faced when I joined IBM. Or let me put that, Let me put that Way, right. So because I was an industry, I mean, I know process, right? So if you, I mean anything you want to do, you have to set up a process. There are people, there are systems. You gotta bring it all together, make it a beautiful flow. Right. Um, uh, so those are my connecting points. If I just cut, copy and paste it, I can do the exact same thing for a marketing process. I can do the same exact thing for a logistical process and you can do the same thing for um, anything. Like a high level decision, uh, making, strategic process. Right. So when you enter a system, I call it a system because a company or like anything, right. So when you enter a company or like uh, when you start seeing things from this like process flow, logical flow, just use like I would say data. Uh, science is just a common sense, right? Why I say that is you just think logically, uh, and you put things together, you connect the dots, you will easily crack it. It doesn't matter what you do or like what, uh, where you are. It doesn't matter. Even coding, like sometimes, uh, you see this, AI engineers doing a lot more crazy stuff, but it's actually nothing but logical thinking. And with a bit of common sense and business understanding, that's it. Right.

Speaker B: So you're seeing patterns that sort of are above the context of that. This is paper and this, you know, and some of the details.

Speaker C: Exactly.

Speaker B: And, and so understanding kind of the broader, you know, broader systems thinking allows you to kind of jump from completely different specific industry contexts and you're able to bring a lot of the same, you know, approaches a lot of the same kinds of decisioning and, and uh, and engineering principles. Yep. Over.

Speaker C: Yeah. Okay, so let me add like a few more, a couple more points as there as well. So being said that I'm also a bit of a, a bit of a social guy. Like I literally uh, go and talk to someone. Like, even though I didn't, uh, even though I did not work with like any of those, I literally go and like talk to them about a process. Like just a small talk, but, but at the back end I tried to ask how it's going and what's going on at the back end and why this is impacted. Right. So, uh, to understand any framework at any situation, I have this five whys. Like what, when, where, why, how. So you ask these questions around anything that's gonna give a clarity to your mind. Okay. And then you can say, yeah, okay, this would work out. Or this doesn't make sense. If it doesn't make sense. Yeah, you re question Them and that's going to keep the conversation going. So asking the right questions, um, you know, and having the social way of like interacting and checking with the people is definitely gonna give you a long way in understanding a lot more complexities on these things. Right. And also one thing in Westrock, what they did is beautiful thing is they took us to their plans. Like we traveled to five plans around Atlanta. So Atlanta is where my internship is. They took us on a bus trip, uh, all the internship, like two full buses. Like school students, they took us through plants and walked us through each and every supply chain, every plant in their supply chain. Starting from production through wastage, uh, they have lost more wastage. Like the recycled peppers going in there. They actually get it from the waste processing plant. We literally went to a waste processing plant. We saw how the waste processing is being done. So being there, I asked the managers on the floor, hey, okay, what's going on here? Right, so any quick questions there? So all those like small, small bits and pieces kind of like sits in these puzzles and you could form, uh, a nice process. Okay. Yeah. Uh, this is how things are happening on a grand scheme and then you go from there. So yeah, that's how they do it in place.

Speaker B: So you got to see the every. Right. Every kinds of. Every kind of processing plant involved in the entire chain.

Speaker C: Yes.

Speaker B: And you were just mentioning just being extremely inquisitive and, and social. I mean you just, you have to talk to people, you have to geek out about, you know, what you're learning here.

Speaker A: Yep.

Speaker B: And being curious and using those questions helps you learn each industry so that you can then m. Go and take the actual data science and apply it.

Speaker A: This podcast is powered by Brando, a digital marketing agency that not only provides a diverse mix of digital services, but offers guidance, experience and true partnership to bring your business to the next level. This episode is about artificial intelligence. But, uh, we also offer expertise in other areas such as paid media, SEO, uh, development, analytics, data science and design, just to name a few. We've worked with businesses on every level, from local operations to Fortune 500 companies across a variety of industries. We have experts to structure, strategize, and execute your digital marketing needs. No matter what step in the process you're at, we're there for you. From wireframes to paid campaigns. Brando, experts in everything digital, please reach out today to learn more about how we can help your business grow.

Speaker B: Okay, so Westrock. Mhm. Tell us a little bit more about, you know, where you went next.

Speaker C: Mhm. Yeah. Sure. So next, when I was interning at Westrock and because it's my first experience, um, I got another interview from Raof, OK Southern, which is again same in the Atlanta region. And uh, uh, luckily our school, like one of the reason I selected University at Buffalo. Like sunny Buffalo, New Yorkers, they allow you to work like six months. Like not many universities do that. So few universities, they don't let you uh, during the fall semester they don't let you do internships. But my school does it. And also uh, I was closely working with a professor who was also luckily on the transportation. Professor. So who was on the transportation studies.

Speaker B: So you're at University of Buffalo, but your internship is in Atlanta. So are you taking classes online? Like how do you.

Speaker C: No, I don't take any classes actually.

Speaker B: Okay, so when you were doing the internship you were just pure internship. That was your main. Okay, that was your main focus?

Speaker C: Yeah, pure full time. It's a full time internship. And summer was fine because summer was holidays anyway. But full time is actually you can make this.

Speaker B: Uh.

Speaker C: Uh, I pushed my courses because uh, I was doing a thesis and thesis have certain credits and uh, instead of doing the course I picked up the thesis. And this is like a year long thesis. You have to do uh, work on a paper with the professor, uh, and you defend the thesis. Right. So that's exactly how it goes. Uh, I picked up that option because I want to do an internship as well and also thesis. So I wanted to maximize my uh, um, inner student master's life and make it come out like as a successful uh, as a student. Right. So I did that great thesis.

Speaker B: And you're also going to have great experience.

Speaker C: Exactly.

Speaker B: A broader network.

Speaker C: Exactly. And um. Yeah. So quickly back to the Norfolk Southern experience. I gave interview and I was selected. That's great. The moment I enter Norfolk Southern, it's actually a fright. I mean I think uh, if people don't know about. Norfolk Southern is nothing but a freight company, uh, a railway freight network. And they operate like close uh, to ten thousands of miles of track. Uh, they have the rail tracks and also they own the tracks as well as the locomotives and all the items that. I mean all the logistics. It's part of logistics. Exactly. Now we're on the trains and uh, uh, literally all the movement from California to all the bulk movements from east to coast, they move via trains in us. So there are some eight railroads who control the logistics of across us. And of course within is one of them. They have like pretty strong network on the east coast and from Maine through uh, Florida. Uh, right. And that they're. They. And also they run to Texas and I think Arizona as well. So they have like a huge network. The moment I went, I mean I didn't have any idea how good or like, I mean what sort of caliber they have, but the moment I went there, oh my God. They are one of the extremely sophisticated team technically. And uh, they're almost like ah, it's like a Google actually. Um, the moment I get into Google, you see everyone is like super smart. That's the kind of the team I got into there. And oh my God, it's like I felt like, okay, I'm not even to that part. Like I knew it. I don't know. I don't even know how I got up there. Right. So that was my experience because they were doing computer vision on. There is a computer vision camera in their locomotive and they exactly spot any issues on the tracks, right? And that is like in 2016, right. And you guys see the caliber, right? And they're like pretty much, pretty much advanced in the mathematics. Uh, and A.I. they have a lot of satellite data. So the power project I was involved in, they have this satellite data picture of the track being taken like at every mile and they visually inspected. So the models that see the images, that's like millions of images and it pinpoints exactly what is the life on the track. So there is a um, geometry of the track that does like a T shape and it kinds of erodes over time because obviously as the trains run it kind of erodes and it's up to the uh, we were working with the predictive maintenance team and uh, obviously before anything bad happens, you have to go fix that because if something happens it's almost uh, a two week delay, right? Because you got to fix the track and redo again, do the test runs and whatnot. Right? And it could be anywhere. Like you had to travel thousands of miles to get the products there. And so it's like a big headache for a freight company. So very sophisticated algorithms, very sophisticated big data systems and uh, sophisticated teams as well. Like um, most of them were Georgia Tech and uh, Georgia Tech and some MIT folks too. M extremely smart, right. So sometimes I couldn't even get their language, what they're talking about. You know, it was because it's internship and like three month time, right. So like. But I did a bit of justice because I was literally doing the data cleaning, uh, data cleaning of the satellite images that's generated, you know. And also I was Verifying the models model output with the satellite images, what is actually predicted, which is what is actual and then compare it as it currently flagged or not. Right. So uh, uh, even though you know, I wasn't um, you know I wasn't exactly involved in the core model development but I was validating the model doing another like um, uh, close to a natural language processing work where uh, engineers are type up a text and it just straight up goes to uh, a predictive counter to say okay, this locomotive might need a predictive maintenance, uh, check it up before the next trip, something like that. So huge lot of work.

Speaker B: Um, you're trying to take all the signal that's possible using satellite signal, you're using cameras, you're using text. Yes.

Speaker C: Literally all the technology you could think of.

Speaker B: All the signal. All the signal, yes. Uh, we need to fix this segment of track at mile 13, you know, 0.2 on this line.

Speaker C: Yep, yep.

Speaker B: Uh, because it's below our threshold of.

Speaker C: Exactly. Yep, exactly. That's the math. That's the thing I'm assuming. But a lot of mathematics.

Speaker B: Um, you're learning all of this in a three month. Three month. You're learning enough that you're able to articulate all of this.

Speaker C: Exactly. I uh, couldn't comprehend. Yeah. But yeah, it was great. But it was a great experience post that. Mhm.

Speaker B: Your, your internship experiences are extremely remarkable. I think. Um, I, you know. So you have to be feeling pretty confident now as you are approaching the end of your grad degree. Uh, you're ready to go and enter the workforce again. Yeah. You know, full time, not as an intern. Tell me a bit about that.

Speaker C: Yeah, sure. So now I was like trying to do the full time hunting. Job hunting is the most stressful time for 20 masters or any student. I would put it right.

Speaker B: Is there a decision science that you were putting into your job hunt? Like do you kind of like think through the factors of where to go next?

Speaker C: Uh, I didn't know that way. But uh, the emotions were high. Uh so I didn't nerd out that way. But uh, I was confident. Uh as you exactly said, I have pretty decent experience from IBM, um, and a pretty uh, good course, um, uh, great internships. So I have pretty good on my profile and I know if I crack the interview I think I could get it right. And I indeed gave a lot more many interviews um, uh, somewhere like because also I was also trying, I'm from industrial engineering background. But uh, one thing I still uh, uh, kind of don't have is the proper coding skills of a machine learning scientist. Because uh, machine learning scientists are usually computer science folks who have been learning all the data structures, all the hardcore algorithm. Um.

Speaker B: Right. They design data structures.

Speaker C: Exactly. So they design end to end systems. Uh, uh, they have coding at an operating systems level. It's super about coding stuff. Right. So I don't have that skill set. So.

Speaker B: Gotcha.

Speaker C: Um, uh, and obviously it's like an engineering thinking. Only if you did computer uh science you would have that thinking sort of thinking or would be good at it. Uh, uh, and um, somehow uh, I couldn't pick it up in a shorter period of time. Let me put that way. Right, Right. What I had to do was okay, I know my coding game is not that great, but I know my business game is really good. So I just used that to my leverage. And all the different conversations I had different. A lot of there were coding rounds and um, there were multiple strategic rounds and like uh, uh, HR rounds and like the behavior rounds. I uh, was putting myself as a strong business and strategic person there than more on the inner coding and like the other things. So, so that um, level of thinking and like that level of um, you know I would say marketing. Right. It's a self marketing. You gotta market yourself, think why. Exactly. Why you're the best. So I did my best to. So you know, it's kind of like uh, you gotta drive the interview your way, not let the interviews take the control of it. So there is a bit of a uh, smartest unit to play in the sense, understand who's gonna interview you, um, review uh, their LinkedIn profile. If they are super strong in something you don't touch there because you might get stuck there in the sense. Um, because yes.

Speaker B: You're optimizing.

Speaker C: Exactly, exactly. Data science is like quite, it's a quite vast. And also there are many experts who are interviewing this. Right. So they know algorithms in and out. And without you actually going knowing end to end of an algorithm, you cannot uh, say okay, hey, I worked on it, right. And uh, you uh, might get sets. Even though that's not a uh, one deciding criteria for like for selecting you as a candidate. But still like that's kind of a basics. Right?

Speaker B: Well you're bringing up a great point here. There are different career paths and some of them involve coding skills. Yeah. Some of them are more strategic and maybe industry thinking in nature. Systems thinking in nature. Um, I'm curious about you know in your track where you said you were kind of approaching this with less coding. I imagine you still have to work with the data, so.

Speaker C: Yes, yes.

Speaker B: How do you interface with it? What kinds of tools do you use if you're not just writing straight code?

Speaker C: Yeah, so it's uh, I would put it like um, the Python level coding is not like earlier at my stage, okay. Early when I was graduating uh, from a master's. I'm not great at Python level coding. Like Python or R programming was two languages of my choice. I had that time. Exactly. So I was using R because that's good for statistics and uh, that's good for doing the analysis. So I'm good at writing up the analysis but not an end to end production level. Great software or like systems. So that is what I mean as like a hardcore coding side of things. But I do know like the SQL. Like SQL, I know the SQL and I've been working on the SQL, uh, even in IBM. So I was all good at it. Uh, so the basic level of up until data analysis, I'm good at it, but not at a data scientist level coding. Right, gotcha. Um, uh, because I didn't have that experience anywhere yet so far. Right. Uh, you can do the analysis on hand or like in paper or maybe in like as Excel sheets in Excel. But um, in order to get that level of experience you have to really work through it, which I got only during the inter, during the internship time. So I did have some experience on the internship time but not to the level of like you know, at an expert level. Now why I'm saying that this is I interviewed for, I even interviewed for Meta, in fact, Facebook. So being in industrial engineering and you interviewing for like a Facebook or Facebook. It was Facebook by the time. Right. Interviewing for Facebook. I interviewed for Adobe. Uh, I interviewed for ebay. Um, I interviewed for many, I would put it like the California tech companies. Right? Yes, that was my initial target. But then I quickly figured I didn't, you know, maybe I need uh, I need some more time to get there. Because they were expecting raw coding skills.

Speaker B: Like they didn't just, they were doing coding tests in their interviews or something like how exactly.

Speaker C: Yes, exactly. It's a live coding bug, this. Exactly, exactly. Live coding interviews. Um, a, uh, few startups as well. But their pay, obviously the pay is like the best. Right? So they were ready to pay 200k, 250k. But then they were expecting like a computer science background. Like even though you do have like supply chain business like business skills and like knowledge or strategies, but they were looking for, you know, uh, they want the raw Coding skills. They want to prioritize the coding. Even though you don't know about the business.

Speaker B: They, they're looking like you said. And, and, and it sounds to me like you're doing a lot of decision scientists science work that doesn't require you to be a particular, you know, technician at just the coding part.

Speaker C: Exactly.

Speaker B: You know, and, and you do know some technical skills. I mean you do, do, you do use R, you do use Python. Um, you do work with databases of various types and you do write SQL. So you're very much in data.

Speaker C: Yes, yes.

Speaker B: Um, but you're saying that there's a strong career path for people who are focusing on the industry, on the business. Not just the raw, you know, technician skills, but strategic skills. And that's, and that's where you've been building a career.

Speaker C: Exactly. So tell us, let me put this right. So I was trying to find the fit, right. So uh, to put it cleanly so this job would fit my profile. And I was clearly, by the end of the process I clearly know what job I should apply to and what job I shouldn't. Right?

Speaker B: Yes.

Speaker C: And uh, in fact that kind of proves my point because um, even at times right now I give, I apply for certain jobs, I get a call immediately because I know that profile exactly fits my profile and what I want to do next. So I had that clarity. I mean it took some time for me to reach that clarity. I was interviewing for a couple of months but when I gave the Staples round, you wouldn't believe it was like three day interview. Uh, they were hiring quick and they wanted to have a lock up, uh, a lock up a candidate. And uh, it was just three days. By the end of the fourth day I had my offer. And uh, they didn't require like a master coders. No, but they clearly know they need some uh, they need an analyst who can think like strategically at a systemic thinking, who knows the business, you know, um, all those sort of things is where like it ended up like giving me a job at Staples. And it just meant everyone was like super happy the very first interview, very positive. It was like a quick, uh, super quick and in four days I got my, from the first round to last round, like I literally got in like four days.

Speaker B: So okay, so four day multi round interview, intense, intense interview schedule. Like a rapid interview schedule for Staples. Staples has a lot of stores. I don't know how many they have right now but ah, they're a very large, thousands of stores. Um, and you're working as a data Scientist for Staples. So what kinds of problems and challenges, industrial engineering challenges are you solving with data science at Staples?

Speaker C: Yeah, sure. So, um, let me put this. I wouldn't call it an industrial engineering problem yet, but uh. Because I pivoted more to an operations research and like the data analytics part of it. Exactly. I mean, let me put this. The decision science is where like I'm, I'm pivoted most of my energy towards because I clearly know that's where my success is and where I would, I would go as a Prof. I would grow as a, um, as a decision scientist than rather being on the, on a different, uh, different path. Right. Because it's a specific domain, you need certain knowledge and you can go much more deeper into it. And, and any day I know there's going to be demand for it, uh, because of the large, because of the large set of problems being solved today. Right. Every day, like day in and day

Speaker B: out making decisions is. Yes. There's always going to be value in someone who.

Speaker C: Exactly. So, um, and exactly that's what my focus is like. Initially I was focusing on um, uh, uh, all of my first year I was working on this financial model and my team has developed and they wanted an analyst to report and closely work with the merchandising team and supply chain team and bring up the data, bring up the insights and Show hey, what SKUs are profitable or not profitable. Right. So that was my very first project and literally I worked it for a year because there is so much insights. Uh, there are like 50,000 SKUs that I need to analyze across different, across 20 fulfillment centers. You can put in the, that the combination I have now I need to analyze at a network level what SKUs are profitable or not profitable. And you have like the moment you get into the real world, you know, like all data is like messy and like it sits in like different places and um, fulfillment team, uh, giving an expense sheet in a different format, uh, a transportation team is coming in and the worst part is no one know what's the right answer to. So at times this also happens and you have to dig up yourself, do the analysis yourself. And well, I love that time. Right? Uh, uh, you don't have any a tools or anything. It's a raw thinking from your end and purely from your team's guidance, uh, and direction. Uh, we literally had like one day, one whole day we had a brainstorming session. I never know that would even like happen because um, uh, we were constantly talking just about data. We just had like three Data points and multiple insights from multiple systems. And we were discussing all day to reach a final recommendation with my manager. So those are like the amazing times, you know, like we um. And as I said, like the specific decision, whatever's working towards us, okay, should we carry this product or not in Staples and because it's doing profitable. It's not profitable. Uh, if it's not profitable, why it's not profitable. Where we are linking it. Is it on the promotional skus? Is it on the uh, um, uh, uh. Because there are many products, uh, we buy only for specific clients. What's going on there? Maybe they initially promised and they didn't continue with the volume. There are multiple things, uh, I need to closely work with the merchandising team to understand what's going on at the back end.

Speaker B: Yeah, I mean I think about all the variables that you had to deal with here. M. Because you have so many different product lines and so many different dynamics in play. Like you said, the costs, the total cost to the company to have that SKU in its register as something they offer and then what you sell it for and what kind of. What's the real margin after? All the other logistical expenses involved in just having that skew in the system.

Speaker C: Mhm.

Speaker B: Are calculated and you're trying to figure out where are they winning and where are they not.

Speaker C: Yes.

Speaker B: Um, okay, so this is like an extreme version of that raw material project that you were working on, your first internship.

Speaker C: Yes.

Speaker B: But uh, it's for Staples now and you're learning a lot and it feels like it's closest to what you know, your whole intention, which was, you know, to. When you were at IBM M, you felt like you weren't on a track and now you're definitely doing the thing. Yep. That you were, that you were aiming to do. Uh, tell me about, you know, your, your time at Staples. You, you know, you decided to move on. Tell me a little bit about. Okay, where do we go from here? Yeah, you've kind of done it now. You check. You did exactly what you set out to do. Now what?

Speaker C: Yeah, so as an operations analyst in Staples, as my first job, um, there I learned how to build this cost to server analytics and understand more from like we uh, were like an internal consulting team. So um, and I did like a bunch of other projects as well. Like specifically closely. One specific marketing project we did is um. Okay, you show products in the website only those which are closer to you, closer to the customer. Right. So they'll be able to order only if it Is for example if I'm sitting in Pennsylvania, uh, I would have option only to select from uh, Pennsylvania. It will show only the products which are in stock from the Pennsylvania and dc. So.

Speaker B: Gotcha.

Speaker C: Uh, uh, in all those like sort of like I did like web ranking. There are multiple small, small projects we did. But then later I pivoted more into a data science role as a natural progression. That's where I got into the core inventory optimization project. Which is exactly what I thought I would be doing during my bachelor's time. Right. So it's kind of like the full circle moment for me and the moment I got into the inventory optimization. Oh my God. Yeah, it's a crazy same 50,000 SKUs into 20 fulfillment centers. It's like a lot of firefighting and ah, data work. Um, constant decisions about where should we stock the products and how much should we stock it and what is the safety stock on it, when should we order. Right. Um, and we closely work with the inventory planners and demand planners team and they kind of like own certain matrices and we plug those numbers and then we try to develop this model to say hey, okay, yeah, this is how you should carry. And as part of this we also handle the transfers. So which is like, yeah, if I don't have products in California D.C. then you move certain from like non, uh, moving DCs. Like for example, if uh, Florida has more quantity of the same SKU, then you tend to, you know, move it to the California D.C. or to the Seattle D.C. or to the Boston D.C. fulfillment centers. Yeah, exactly. So uh, I worked up there. I was an owner of that project. The another project I also want to offer is the uh, ranking. Right. So how fast should the products get into the fulfillment centers? So touching this inventory gave me a perspective of end to end supply chain. So I called myself an end to end guy because I worked on inbound fulfillment inside fulfillment centers. What's going on? Labor planning, uh, outbound and transportation including first mile and last mile transportation. So I worked on like I touched up all the domains being there and that's kind of when I felt okay, I have two options here. Either I can grow into my more profile, uh, and I would become uh, like a senior manager and director and then go up a profile. Right. And yeah, kudos to my Staples team. My boss was like super happy to do that. But then I felt like um, okay, maybe I need to uh, pivot and get into a different supply chain to see what's going on. So I was, I wanted, so I wanted to get some different experience and see, okay, maybe redo to check. Um, you know, I uh, always have this hunch for like I want to move to California because I have a lot more friends there. And also I was like, I wanted to somehow get in there to see what's the vibe there. Right? Because all are a lot more innovators, lot more coders. Um, you know, the passion is high, energy is high and I wanted to be there, simple as that. So. And I gave interviews, I gave a beautiful company called Keep Trucking and again they were expecting hardcore coding again. Uh, and that moment I felt like, okay, you know what, it's too much for me. Uh, let me stick to the business and strategic side. And then I got meaning the decision science side. And that time, luckily, uh, I wanted to work for a startup. That's why in fact I was trying to get into California. Luckily, American Eagle Outfitters were hiring for their startups. Right. And that's how I got into it. And wow. And the team was like amazing. I felt really great because, uh, a PhD from MIT, the smartest out of all, I would put it right. He is the one who interviewed me and he gave very good feedback about my profile and my thought process towards my business and how I'm approaching towards this decision, decision science as a whole. And yeah, boom. I think I felt like that's kind of my certified inner blue tick moment in my profile. Um, and yeah, I got in there, oh my God, it was a roller coaster right there. Um, starting like it exactly, roller coaster. I went like super high, high, high. In the first year I was doing presentations, I owned this transportation cost model. I'm responsible for giving up the sharing the cost. Like for example, if a package going from A to B, from a zip code to B zip code, what is the exact cost, how much it would make? And uh, the pricing team was exactly using my cost numbers to add up a margin to share it with the different clients. Because it's a third party logistics, we do this. Uh, I took care of the transportation cost model and I developed the model, the back end and oh my God, help. It was crazy, right?

Speaker B: Very high level. So at Staples, you're doing everything for Staples. At uh, this new company, you are building software and building sort of a logistics engine, so to speak, that obviously you're connected to American Eagle Outfitters, the popular brand that a lot of our audience knows. But it sounds like you were building a system that you were also able to resell or white label or otherwise offer similar logistics services.

Speaker A: Huh.

Speaker B: It's around 2022. Uh, retail got completely upended with COVID They saw most of their sales happen online. Everything is about online. Yeah, but you're entering it right as. But wait, the malls are opening back up again and we also have like traditional retail is not completely gone either.

Speaker C: Radiolo Calypse. Yeah, right.

Speaker B: And so you have a lot of like you know, transition happening basically they're all sort of in the whiplash of how big their online businesses got very quickly and rapidly during COVID to reintegrating with brick and mortar and other things all happening at the same time. So just you know, help people understand a little bit about this startup like environment. Even though you're technically attached to a known, big known brand.

Speaker C: Mhm. Yeah, sure. So this uh, startup environment is literally a startup environment. So I would put it right. So we were at associated with the American Eagle outfitters. Like like our email IDs were quite different because it was quite platforms. Um, American Eagle bought two startups from. One from like Boston Quiet Logistics, uh, and other from like Afterra from Seattle. Right. And they put it together to create this third party logistics and share American Eagle capabilities. Uh, but the team which was hired to build this is completely new. Like literally people veterans from Amazon, like literally all of like the IT tech team is from Amazon like 10 years, 15 years. Who built the systems, uh, which is exactly powering the pest system. And I'm glad I'm part of that team because they wanted uh, someone to work on the decision science side. And uh, I was put up there and working with a few other scientists as well. So we are a big team. And day in and day out the culture is like morning, we discuss something and by end of the day or at least the very next day we have to present the model's output. So um, that was rapid, right?

Speaker B: Like a rapid iteration.

Speaker C: Rapid, rapid like crazy. And at times we were discussing in the weekend so it kind of blurred. I mean I wouldn't call it a work life. I cannot talk about a work life balance there because it's, it's uh, it is. I wouldn't call it a toxic but it is. If you are on the learning side. This is like the best. And I am ready. I was ready to. Well my girlfriend then. Girlfriend or wife. She was completely supportive. Uh, we wouldn't uh, I wasn't like stripping out a lot. Like literally I was glued to my screen like all day. Literally. I had like four, a few, three or two hours sleeping days too. Um, the very first year was like it was like a running race and we didn't had a single time rest. Right. So we were so focused. Our CEO was active. Like I know we had a call at like 2:00am in the morning. No one would believe, like 2:00am in the morning because, uh, there was an investor meeting coming up and I was presenting some values to the president. And literally because I was responsible, I felt like baggage. Right. That baggage is on me and I wanted to deliver. It doesn't uh, matter. I, uh, know I'm losing my seat. That's fine. Right. So we do multiple, like very quick iterations. Um, we discuss about it in couple of hours. We do an iteration, we do a redo again, back and forth.

Speaker B: Pacing and exposure to that kind of pacing is also kind of amazing. And I had a bit of that early in my career and I could say that that helped, you know, that intensity. Even if it's a short period of your career.

Speaker C: Yep.

Speaker B: I wouldn't give that up. You know, I'm glad I got exposed to it.

Speaker C: Yes.

Speaker B: Um, but to your point, it's very different than what you said described as really comfortable at IBM, you know, um, uh, Staples too.

Speaker C: I would put it like Staples too. I was super comfortable to leave the job in, you know, wo too. So. But yeah, this, this, yeah, I gotta blame my passion. But yeah, it's, it's, it's kind of keeping me pushing and like running. See, see next things.

Speaker B: Okay. And so, and then uh, you know that kind of got you exposure to this startup world and pace and now you're, you're in the midst of a startup. I don't know how much of that uh, you'd like to share with the audience.

Speaker C: Sure, sure. So I'm okay with that. So it's like the roller coaster, right? So as usual the startups comes with the risk. Uh, on the both side you learn a lot. At the same time, the risk is a lot because uh, it's investing and like investors there is a 50, 50 chance again, right. So it can go hit or miss. And unfortunately it was a miss and somehow we couldn't pick it up. So yeah, my role was like eliminated then and um, by the end of 2024. But at the same time my wife was like, yeah, almost into six months of pregnancy. So I was like, you know, um, let me pause here. Right. So because it was, came as a shocker, um, and I was ready to deliver and obviously clearly I'm talented enough to deliver for American Eagle, but uh, they are like already at full capacity and they Wanted to run lean, uh, as you know um, post Covid, literally all the retail firms, they built up a, ah, huge capacity and they couldn't like, you know, um, they couldn't like survive good as per their balance sheet. And because of that huge backlash across all retail industry as a whole. So um, it's just a cycle. I think retail goes through the cycle every few years and it's just part of the cycle. I think that's another thing you want to understand if you're on a layoff, it's just a business cycle and it's not purely about you. Right. So I clearly understand that and I want to take a pause and because my wife's in the pregnancy I wanted to spend some time, good time with her and take care of her. So I spent that time and our beautiful boy arrived. Um, thank you so much. And uh, since then I'm almost like a full time father for like the first few months and had a beautiful time and enjoyed with the guys. And right now at the back end I was doing couple of um, I was exploring uh, what I'm really good at and what I want to really do for the rest of my life and I figured out okay, let me look into some startups, right? So let's, I think because I worked at a startup and I know what is the culture that we need to build and what is the energy you need that to drive to the next step. So I started collaborating with a few of my friends and also independently I did consult a couple of brands to share my expertise and do a bunch of analysis around there. I did that. Uh, but I want to focus on a bigger idea like see, okay, what is the next, which idea is going to rule next 10 years, right? That's what I'm looking at, right. And I'm not looking at quick wins. So uh, because I'm not trying to rush at this point because thank God I have very supportive partner and financially I'm decent, I've said up enough I guess. Uh, so it kind of gives me that question to say okay, what really I want to do um, in this whole thing, right? So that's when I figured okay, I need not to rush. I know everyone is going after AI AI. Um, but there is also another uh, bigger domain like energy that's being picked up because you need energy to have AI and energy is a place where a lot of decision science is needed uh, at this moment like exactly like a retail distribution, you have energy distribution as well. Like it's get created at the Source and it's being split, uh, through every substation, uh, down to your home. It's exactly like an E commerce supply chain. And I can apply the same skill sets, literally the same models I can use there as well. So I'm literally looking into, uh, different, even data centers. Right. So data centers, how they receive power has more of a decision science to it. So I'm exploring decision science not just on a supply chain, retail side. Now I want to go beyond the supply chain and retail, see, see where I can contribute. Right. And what I can do. Big. Right. As I said, which what idea going to be in Trend in next 10 years is what I want to work towards. And, uh, yeah, that's what I'm doing right now.

Speaker B: So when we think about all of this and we think about, like you said, AI is obviously the big decision of the day. You know, are we going to use it? Where are we going to use it? How are we going to use it? Um, and to your point, there are a lot of industry business problems that are sort of popping up as a result of change in what's going on in the landscape. And you're looking at this and saying, I noticed that this whole thing that's happening over there is creating an energy problem and that's actually where there's great work and great problems to be solved.

Speaker C: Huh?

Speaker B: Uh-huh. And so your viewpoint of this is a little bit differently than a lot of the folks that are just chasing to go and figure out how to use AI. You're saying AI is the customer and, uh, you know, and how do I, how do I make sure that, you know, we had the infrastructure to support it and that there's opportunity over there. So you're still looking at things, again, a little bit differently, looking at the, from a systems perspective to kind of find the right career path pieces here.

Speaker A: Yep.

Speaker B: I know. You know, before you mentioned you didn't develop a lot of those coding skills, and now we're seeing a lot of these AI tools help people do coding. Um, yes. I'm curious what your thoughts are for people in analytics, in math, in analytics, in data science, in decision science. What kinds of things should they be investing in? What kinds of perspective can you give to professionals in the space about what they should be looking at or thinking about differently?

Speaker C: Sure, sure. Let me say this. Yes. With AI, um, my skill set. Right. So the one which was lacking all this while. Right. Uh, well, the hardcore coding, right. I call it a hardcore coding, uh, level that's been level set. Right. So AI is actually a level playing field is what like everyone is saying, right? So uh, you don't need to know coding. But obviously it's not like I can develop an end to end application, but right now I have the superpower in my hand saying, hey, okay, any problem, I can do the work. Because coding is being done by AI and it's doing the pretty good job, right? So with just $20 subscription, it's like doing amazing job. So for experts like me, um, it's definitely a great tool. Uh, and to test that I gave literally a few interviews. Um, even though I'm doing all those things, I literally, I do interviews because I want to stay relevant and see okay, what's, what's going on, um, what's in demand, right? So I want to see what's that side as well. So uh, for uh, the last two, three companies I see coding is becoming, becoming optional. Or you can use AI to code the rounds. So coding round is still there, but they're letting you to use OpenAI. So it's like open book test, right? So you can literally. So they are going after your thinking. So how is your thinking looking? Like, so that's exactly. And your attitude towards the problem solving is exactly what the teams are like looking forward. So which is I'm super glad, like if I'm graduating right now, if I don't have the coding skills I need not to worry about it because I can get into any company right now, right? So that being said, the job I did just that in like 2016 has become obsolete. So I'll say this. Let me um, for the audience of like, like uh, let me split into two parts. One is like the freshers, like the freshers who are like ex, you know, coming out of the uh, schools, um, you guys are like pretty good at like picking up that technology because I think it's, it's Gen Z and like, um, you know, Gen Alphas, like ah, you know, those are the people who are like working on the, on the school age and like coming out of the school, you guys, you guys pick up technology real fast. Uh, so Sam Alpine also confirms that you guys are the luckiest people right now to graduate. Right? Uh, even though a lot of um, entry level positions are being kind of eliminated, but it's kind of transforming the role. Right? So how do I. Job can be anything, right? Kind of job or agnostic AI skill set is what you have to develop in the sense, uh, take up an AI, be it a Gemini or OpenAI anything, spend all the Time with that, like, ask questions. You know, AI Is actual. It's like an S man. Like, whatever you ask, it's gonna say, yeah, uh, yeah, S. Boss. Yeah, you're right. You're the king. Yes. But you challenge that, right? You challenge all the way. You challenge. Whenever you give a prompt always come from the go for go to the standpoint of like, challenge it, right? Uh, challenge it. This question is wrong, right? Why you need to do that is why I'm saying that is you're learning. I mean, the new people, uh, the younger generations are learning is going to be with A.I. uh, I'm much more worried about my son's A.I. too. But, uh, using A.I. and like, whatnot too because, uh, you do not know at what level they need to stop. Right? That's also a thing. Right. But do this have your foundation super strong. Right. By oppositely, ah, questioning this A.I. right. Okay. Yeah. Talk about the quantum physics.

Speaker A: Yeah.

Speaker C: Okay. It gives pages and pages of like, references, materials, everything. Yeah. Boom.

Speaker A: Yeah.

Speaker C: You have the knowledge, you read about it, you have the knowledge. Now you, you, you, you take another step. You, you ask questions out of it, right. You challenge it. Say, you say it's wrong, say it's wrong. It's gonna give you a supporting evidence and like a lot more. More. It's gonna take you a conversation much more deeper to you. So that's exactly how, you know, I, I got my knowledge. Talking to people, picking up their thoughts. Um, you know, when a leader is speaking, they exactly point out a metric. Why I'm not. When I was talking about staples, they talk about one metric. But I'm not working. Why I'm not working towards it. So all these, like, kind of inquisitiveness will kind of fuel you to the path of like, yeah. Understanding more and more about the systems. Right. So be it. Like any business doesn't matter where you're getting into, but, you know, try to get the basics done first. That's going to be the key. Like the math and bare basics. I know it's going to be boring, but, you know, try and like, you know, to, you know, get, get to the core of it. Because if you, um, if you are so clear about, like myself, like, I know I'm getting into the supply chain, I wouldn't change my domain in the sense like, um, the decision science is kind of my domain, to be honest. Like, uh. And I'm not, I'm not pivoting to, uh. I'm not pivoting from this ever. Even though A.I. yeah.

Speaker B: What, um, what hm. Skills are still required even if you have AI? Uh, mhm.

Speaker C: Yeah. Business skill sets and systems thinking. Um, well clearly no, when you're doing an analytics, right, ah, ask the right questions. So I would say analytics is actually asking the right questions. That's it. So everyone have the answer. I mean you have answers like AI has answers. Everyone have answers. But are you asking the right questions? So. Exactly. That is the way how, ah, uh, an actionable analytics analysis being done and being propagated. Exactly. Understand where the impact is going, right? The impact of your number. When you're changing in something or you're doing some analysis. Ask where it's going or who is consuming it and understand the metrics. So, so in today's organization, right everyone is like operating in like silos, like data silos. It's still there. That problem is still there. When I was working for Staples, be it American Eagle, each, every team are on their own. Like they are like uh, close to silos, just within them and they're experts just within that boundary but they kind of not share with the outside world. Well what I'm saying is like go beyond that door. Like I know that silo because these silos are cultural, sometimes like political, you know, uh, tayed. It's a lot more come from this cultural standpoint. But I would say break that right? Go for it. Um, don't be hesitant to ask any questions. So that's exactly how you sort of expand your knowledge and you get deeper into it. Right. So they're calling it um, a T way. Right? So um, you have a skill set maybe on like uh, one or two topics, uh, but then you go deep into one topic. So it's exactly like the T formation, right? Exactly what you got to do. And being in this, I'll say this, I'm not worried about AI, um, even though it can make my job obsolete in like 10 years going ahead. Uh, I'm not worried about it because I always know uh, my business knowledge is needed for that AI to train on its own to answer my questions. What do I have? Right? So uh, I'm talking about, I'm saying this because um, I've grown to a point of like calling myself um, you know, a professional expert right now. But yeah, pick and choose. I would say pick and choose one specific path, specific domain, learn everything out of it. You know, um, just go, don't go on a horizontal pathway but take up a, pick up a vertical and go straight up into it. Or I see another set of people who Are making really hit really good is uh generalists, uh people are calling an AI generalists so they know end to end about like let's say a horizontal pathway, you know about all the tools and systems.

Speaker B: And I think there's value in both. I think you're making a strong point there. But I do like that idea of that ties for becoming an expert in something. There will always be an opportunity for the expert in that thing.

Speaker C: Yep, yep.

Speaker B: Um, but then being a good generalist means you can connect the dots and I think your systems thinking you have to be able to do both.

Speaker C: Yes, exactly.

Speaker B: Yep. Like you said, the whole end to end supply chain, um, you know, it's kind of the same, you know, the same concept but then going really deep into the problem, you know, is your table, your T part. Exactly.

Speaker C: T part comes into it. And yeah, that's exactly uh, what how to survive in this like post.

Speaker B: So I hope you know, the audience takes away be curious, ask lots of questions, get and lean into the, to the challenges and the problems.

Speaker A: Sure.

Speaker B: Um, ask your what, where, when, why, how, um, and um, you know, and ultimately they shouldn't be as you know there will still be decisions that need to be made and still people in control of making those decisions. So you know, finding the different problems to solve, noticing where, you know, new challenges open up. That's where decisions, aspiring decision scientists should, should go.

Speaker A: Yep.

Speaker B: Well, it's been wonderful talking with you Siraf.

Speaker C: Same here. Same here. Uh, yeah. Thank you so much, Brian. Yeah, it's been a, uh, great. Um, you know I was like thinking through all the past memories is kind of like a reminiscing great things. Uh, I mean good moves and also bad moves I've made in my career so far and being here at this moment. Um, thank you so much for this opportunity too. Uh, and this is my first podcast so I'm happy I'm able to share my thoughts. Um, and yeah, anyone who's listening and wanted to reach out to me, uh, you can talk about anything. Uh, not just supply chain. I talk about marketing front end, like back end part of the business energy. I'm exploring more into energy right now. Um, and anything related to automobile, feel free to reach out to me too.

Speaker A: Yeah, this has been an absolute pleasure, thank you. I especially loved your approach of just the foundation, you know, stay curious, question things challenge. Especially the, the yes man that AI can be. Uh, that is always important. And to see your journey into where you are right now, it, you know, it requires, requires a lot of the stuff that you have gone through and best of luck to you continuing in the future. This has been awesome.

Speaker C: Awesome. Thank you so much, Brandon. Yeah, thank you so much. You know, uh, thank you for data fully too. Um, and that's where we got introduced. So, yeah, keep it going, guys. And yeah, I'll be attending the future sessions too. And thank you so much, Brian, Brandon, for this.

Speaker A: All right, so to our audience, thank you for spending time with us. Learn more about Brain do and our marketing services on our website at www.brain.dew. if you'd like to see more from us, please consider subscribing or giving us a follow and we can be found on our social media, uh, like Instagram, TikTok and LinkedIn at brain do the same handle. And yeah, if you learned something helpful, I'm very glad. And we are going to keep putting out some more content for you in the future to, uh, keep this going. So otherwise we'll see you next time at the Realm Stable.