S5 E9 | Decoding Data Bias, Responsible AI & Women Safety Online with Anusha Dandapani, UNICC

Speaker A: Foreign.

Speaker B: Come back to the Tech Factor podcast by Purple Quarter. It's so good to have Anusha in the house. So thank you so much Anusha for doing this from New York so early your time. Really glad to be having you on our show.

Speaker A: Thank you Rupa. It's my pleasure.

Speaker B: Awesome. This is for the viewers, just so that we give a little bit of introduction, uh, to Anusha. Anusha, you've done your B Tech from University of Madras and then you went on to do your Masters in Business analytics from um, NYU if I'm correct. And you started your career as a programmer, went on to being a business and data analyst and then moved towards actually immersing yourself into data science. Right. So could you talk a little bit about how did this journey go about for you? Because you were part of the BFSI segment and then now you move to United nations um, group as the data sciences and analytics head. So tell us a little more about your journey please.

Speaker A: Definitely Rupa. My journey has been pretty much um, by the book by everybody else, more or less. Uh, but um, the way I would look back and look forward is at um, uh, computer science engineer. I started my career as a trained computer science engineer, uh, graduating from a university in India. And my first job was I landed a job in a software company based out of India. Um, and good luck would have it that they assigned me to the banking and the financial services sector. And um, when I started my journey, my first uh, client who I started my career with happened to be a small community union bank where I was tasked with programming for the ATM machine to dispose cash. And um, this happened to be a bank called unfcu, which is United Nations Federal Credit Union. So my first uh, sort of select statement or like the employee table that I queried started with the employee, the un. Um, I was so familiar with the data set, uh, of the UN and it was always ah, sort of a thought in the back of my mind that someday I will be part of the greater uh, system of the un. But however, like everybody else, I decided to um, test my um, sort of skills and also make sure that my career is very much focused on financial services. Um, I focused on building my domain expertise because coming from a computer science background, you got to understand the concepts of finance and like when it comes to back office, front office and also when it comes to commercial bank versus an investment bank, I focus more on my investment banking, uh, sector and decided to uh, certify myself in securities and derivatives because I landed a job uh, in credit suite where my focus was to focus on front office, the private wealth management dimension, um, of this investment bank. That kind of gave me the um, catalyst or the acceleration that I needed for me to continue to focus on an investment bank sort of um, space. Um, I was lucky, uh, enough that I got the opportunity to work in an investment bank in the Bahamas and then I moved to Luxembourg. Um, then I also spent some time with uh, a bank in Singapore. Uh, but uh, I came back to New, uh, York as luck would have it, and I ended up building my roots here in New York. Um, and I got to JP Morgan and most recently with Barclays. Um, so my role in Barclays was over the 10 years of my time at Barclays evolved over time. So I started with financial control team, then I worked with the middle office team and the front office team. And the later I was part of the crime and the compliance team of Spotlight. Those kind of evolutionary life cycle within the financial services gave me the opportunity to understand the things better in the domain, uh, of finance. Uh, I quickly realized, uh, somewhere around eight years ago I uh, went through a midlife career crisis. I felt like, where am I going? Uh, what is coming my way? Because um, I completely believe in the principle, right, like you need to have those three big bubbles in your uh, career plan. And in my career I had the domain expertise, I had the acumen of technology and like understanding of data. Um, but what was missing is that the T shaped skills, they would say it right, like, you know, you gotta have a flavor of two, uh, uh, sort of flavor the domain expertise and the focus area of the industry. But also a deep in depth knowledge was something uh, that I felt. So I wanted to go through the traditional business school route and I ended up applying for um, business schools that time would have it, ah, that I got pregnant and I claimed the offer that I got from NYU Stern. And um, the dean of NYU Stern wrote back to me asking why I declined the offer. And it was a very interesting question. I was like, okay, I declined because going to be a mother and I have too much going on in my life. And um, uh, he was incredibly kind to bring me for a coffee discussion and gave me the opportunity to focus on a program that perhaps might fit well with my career, the journey that I am m in my life. And he offered me the applied data analytics program that is offered by NYU Stern, which gives me the flavor of both business and data science. And he was incredibly um, kind to coach me to consider that program. So I ended up uh, that program. And once I graduated um, Barclays was also very supportive of my uh, career. They gave me an opportunity to um, manage a data science team and we were focused on artificial intelligence and machine learning and identifying ways to identify um, identify things that can go wrong with the Mac. So I was already focused on uh, artificial intelligence and machine learning in my previous role. Uh, um, and especially if you were in data science and come Covid the luck would have it again, um, I've always been in uh, the back of my mind that I will work for UN someday and um, sometimes over the weekend I go on unjobs.org and keep continuously applying for those maybe I'm not even eligible for because like you know what, what, what would happen. I always had the mindset like you know, you want to throw your in the hat. You never know where you get picked. Right. So uh, uh however uh, I'm also very action oriented uh person and I prefer to focus uh, on action. So I always had this many side projects that I worked in collaboration with my friends in un so that kind of also gave me that um, uh 80, 20 mindset. Anyway uh, at the end uh, what happened is I got um, the opportunity to interview and this role landed on my um, plate and in the middle of pandemic uh, I switched my career and I joined the un. So in this current role, um, the professional journey as you put it rightly is that this is my true uh, uh calling I would say because uh, um, I'm not only helping the organization build the data and the data analytics practice but I'm also supporting them implementation, real time use, bring about artificial intelligence and machine learning to the table and how do we want to get the best of both worlds from the industry knowledge and industry expertise and also how do we want to bring that academic collaboration. So there is a lot of good um, uh sort of leadership experience that brings to the table in this particular role that I'm playing uh, in this organization. So my professional journey, if I were to put it, I rolled from a software programmer to data, uh, science to AI, um, sort of focused person right now.

Speaker B: Wow. So it feels like it's completely uh, like serendipity, right? You started with United nations and you end up being the chief data sciences and analytics officer for the un. Wonderful. Anusha. So if I were to look at it right, so BFSI has very different data sets and what you're solving for maybe for one particular domain. Right. Whereas when you were to look at what you're doing now These are real world problems which um, I think not too many people get an opportunity to look at even the data or forget about solving about it. So could you give us a peek into you know, your life as uh, the Chief Data and Analytics Officer on how do you go about in you know, incorporating data and analysis at your work?

Speaker A: Yes. The role that uh, I play at this moment in this organization, which is unicc, is that we are a cross functional, cross cutting, horizontal organization within the UN system. So our role is for us to not only serve for the digital needs of all the partner agencies who we collaborate and work with, uh, but also incorporate data and analytics in their daily um, sort of work. Uh, that is fitting into the role that I play right now. See um, this particular unit didn't exist, uh, so I'm an incumbent who is trying to bring together um, sort of how do we want to deliver um, better outcomes for our partner agencies who we collaborate and work with so we understand and analyze the impact of our engagement, um, and making sure that the outcomes that we plan to deliver, um, what are those metrics, how do we measure it? And in addition for us um, internally within unicc, my role is also how can I enable our senior stakeholders in better decision making and also how we can make the decision making data driven. Um, because very high level metrics, when you ask somebody what do you do with data, how do you drive data change within an organization gives uh, you the opportunity to measure, it gives you the opportunity to measure efficiency, productivity, how do we want to recover cost and so many things that are not um, looked at it from a decision or a data lens. Uh, we bring that uh, lens to our uh, thought process and in the workflow and also making sure those outcomes that our partner agencies who we work with are ah, clarified and defined. Because all the uh, focus areas within the UN is not, none of them are short term or medium term engagement. These are long, long term engagements that we work with our part agencies and these are real world problems that we are focused on. So in order for us to make sure that we are actually driving impact, we have to be more data driven

Speaker B: than anybody else I can imagine. Would you be able to point out any past projects or current projects that you're working on that you're allowed to talk about?

Speaker A: Yeah, sure. Um, so our focus areas have been um, where do we fit, um, our approaches and um, uh, solutions that we bring to the table in solving real world problems. Uh, perhaps I can bring about two examples uh, of uh, real world problems that we have not only built but also deployed and uh, it's currently being used in production environments uh within the UN system. One is the use case of unitad, uh which is um, a data agency where we are applying artificial intelligence to identify evidences that were captured in the war crime uh that happened in the Middle East. Um, we are using artificial intelligence to not only identify um evidences but also spot things that cannot be uh spotted with human eye because humanly for you to review like you know, um, terabytes, terabytes of data. And then also these are unstructured data sets that you m. Don't usually know how to get insights from. We do apply artificial intelligence and this is a collaborative exercise that we did with Microsoft. And uh, this is a wonderful use case that uh currently is being used for investigative practices. Um this is um, when I say investigative I'm referring to um war crime investigations that uh UN focuses on. The second use case, um, uh is uh one of my favorite use cases that we went all the way from whiteboarding um and sort of ideation to production. Um solution uh this is publicly available. Uh this is something that anybody can at least look at. It's called ida. It is Architect Development Analytics. So what we do in this particular use case or the problem that we are trying to solve, um, our evaluators community within the UN system, they have tons and tons of information that are published in form of PDF reports, these are documents and these are things that are published in unstructured content. Um and it is incredibly challenging for our um evaluator colleagues to assess, find, recommend ah and also kind of come up with the conclusions of what to do with this program and these projects because that is um, incredibly time consuming effort. Um so what we have done is in collaboration with aws we have built a solution which is applying artificial intelligence not only to extract intelligence from this unstructured document, um, but also help our investigators to connect the dots if you may. Not only to extract intelligence but add ah human in the loop and also identifying how that this finding, perhaps what they found in the country of Chile matches with what they have found uh perhaps in Bangladesh. So how does these um data points correlate is a uh very, very informative information for our investigators, not only for the investigator use case but also for the evaluator's use case. So both of them are um very much focused on how we can enable, using data and artificial intelligence, how we can add that um intelligence to the decision making and the workflow that um some of our investigators and uh, evaluators colleague go through how can we improve that uh efficiency. Right. Like you know perhaps it would have taken them days to review uh a content. Now it's less two minutes to identify what's there as an insight in the report. So um, the productivity and bringing um insights to the table, shorter time frame is the uh, the problem that we were very much focused on in solving. And how can we apply artificial intelligence for good um in solving real world problems, uh, whatever goal. Um, in both use cases. Yeah.

Speaker B: Wow, really interesting. So the second one you said is publicly available. Have you seen anybody else putting this uh, to use in other use cases? I mean for um, other purposes? Have you seen people applying it biggest

Speaker A: uh sort of goal or the mandate that our organization is focusing on right now is how can we solve our problems with the real uh mindset. Mean by that is hey, if the wheel is already invented, how do we want to add things to the wheel? This is reinventing the wheel. Yeah. So um, the components that we built uh, with the thought process for both the solutions and also most of the use cases that we are currently focused on to tackle is to make sure that the components are built with a Lego block mindset that some of the components can be repurposed and reused for a different context and for a different use case. And how do we want to bring those reusability, avoiding duplicity, uh, and making sure that the data and the things that we bring to the table are repurposing and reusable for a different uh sort of a stakeholder. Um so we do have those um process not only in implementing but from the design mindset itself. Because if you don't design your solutions to be available for reusability, there is no way for us to track it. Uh, but yes, um, that is one of the thought process that we put in. So one of the uh solution that I spoke about IDA is potentially going to be a multi agency solution that in not just one stakeholder uh community within um, uh UN will be using it. They do see multiple sort of consumers who can benefit off of this uh solution. So we are working on ah, road mapping it. Rupa.

Speaker B: Awesome, awesome. So do you see, you know data is the most talked about right? Everyone's talking about data and everyone's talking about AI and um, gen AI and so many other things. So m. You know sometimes I often wonder do you see any challenges in data implementation in the future? Do you see that impacting uh the industry at large or your organization just wanted to understand that Piece um, that

Speaker A: does keep me up in the night. I would say definitely it's the front and center when it comes to doing uh data science or AI is that, see the bias in data. Bias in data still remains as a big, big sort of a puzzle for uh, every industry to consider and resolve. Yeah when I say bias in data there are various biases that gets embedded when we are uh, not uh um overlooking this biases when we train our models, uh, definitely making sure that the gender equity is considered, uh, making sure that no one is left behind and the diversity of the data that we use has considerations of all um, sort of human rights being considered in the data sets that we use. The bias in data will continue to remain uh, to be a topic uh, that we will um, put in front of us all the time. The second is AI governance. I mean by that is that um, with the pace at which gen AI is progressing um, and also the technology we want to make sure that we don't run into this unforeseen consequences. Uh we want to make sure that unforeseen consequences doesn't happen. Um, unlike the tech startup world right, like because in tech startup you can move fast and break things and uh, like you know, perhaps the motto of Facebook doesn't work uh um when you are delivering uh outcomes for a public sector or in a public organization like the UN system. So um, those are sort of the things that I see as challenges um for data implementation in the future. But uh, um I think those are all solvable um challenges. I'm optimistic about it.

Speaker B: Super curious here. You spoke about bias in data. Data biases, right. So how do you, how do you first of all figure out this is a bias in data and how do you actually have a checkpoint for that?

Speaker A: Bias in data can be um, um not only identified, can also be mitigated in many ways. For example uh, if I take the example of making sure that gender equity is considered in the data sets that you, you're uh working on. Um, if I were to walk through an example, um, is that when we are representing or building a solution that is pertinent to a decision making workflow, all genders needs to be considered and are being represented in the data is the first checkpoint and your training sets and your test sets that you consider for your artificial intelligence algorithm, those guardrails um, should um be implemented from the get go from design how you are going to approach in solving the problem. Most data sets don't have that level of quality of data. But having spelled out that These data sets were not considered, and here's why. And what is the context of the problem that we are looking to solve? Uh, embedding, responsibility, expl. Explainability and traceability are ways to mitigate this kind of a bias that gets built into these artificial intelligence models. So, um, there are ways to mitigate. It's just that we have to be, um, cautious when we are designing this with a responsible mindset. Yeah.

Speaker B: Wow, you really work on interesting stuff. You know, more and more we speak with you. I'm just super jealous that you get to work on this while I'm doing this, asking you questions.

Speaker A: But you are very welcome, Rupa, to join. Yes, yes. And, uh, we welcome everybody and anybody who can support us because the UN we are focused on is not just for the un it's for everybody to, uh, be engaged, to solve for, because the Sustainable Development Goals is set for the world.

Speaker B: Super kind of you to say that, Anusha. So I wanted to bring, uh, the focus back on, uh, the workshop that you had conducted. Right. This was to tackle waste right, in the school. Um, one. How did that come to you guys in terms of, you know, that you needed first the idea of the gap being identified, then the idea and how do you go about resolving it? Could you, uh, tell the audience how did you identify the gap right. From that state? So it'll help us a bit.

Speaker A: Yeah, sure. Um, so I'm a very big believer, uh, that the food should not be wasted. Yeah. There are parts of the world where the food is not available in plenty. And, um, in the public school system here in United States, especially here in New York, uh, the food get given for free. Yeah. The food that children in the schools get, it's for free. And it's not like, you know, um, not nutritious. It's super nutritious. Uh, very well thought through. Food that gets given to these children. And if you look at it, um, if you look at the data and also look at the percentage of food that gets wasted, 40% of the food that gets given to these children, um, on a daily basis gets either discarded or wasted and not being repurposed. Yeah. So how did we want to solve this problem was that we reached out to the New York City mayor's office colleagues, and, um, they gave us the opportunity to identify five schools from five boroughs. Um, and especially, uh, kids who have this sort of acumen for c. They want to problem. So what we did is we came together with few data volunteers and in collaboration with, uh, itu, uh, at the United nations and also the New York City Mayor's office. And at that time I was with Barclays and some of our volunteer colleagues from Barclays also came together. We came up with a thought process and a framework that hey, we want to teach the students how to capture data if we give them the ownership of identifying or surveying how their school lunch hall looks like or how the food that's being given to them looks like over a period of time. So we gave them a challenge for 45 days for them to capture the data and also understand how the food that is being consumed and how it's being discarded. Uh, and they came up with all this data collection or the observations that they have made as a scientist, uh, they were able to bring that raw data to a workshop that we did, uh, uh, here in New York City, wherein we not only helped them look at the data and analyze the data, we guided them how to come up with a solution in solving the problem that they think is a problem that is solvable. Because we can impose many solutions, uh, when it comes to real world problems. But if the real world problems are well thought through, thought through by the stakeholders who need to action on this problem, it is a different kind of a solution that you can, you could actually come with. And even more so if you make that solution data driven, there is more evidences to prove that why you need to do this. The why is so clear that there is no need for you to prioritize to implement it. Right. Like, you know, it's given that, uh, you have proven with evidence that why this solution fits the need or fits the purpose or why this needs to be prioritized as the first two, uh, uh, projects and um, very interesting with extraordinary insights. The students came up with amazing viewpoint that we could have not thought through if we were to like, you know, do the traditional thinking. Um, they came up with the path process that hey, by giving us a to go container for free in our cafeteria or in our lunch halls, instead of us wasting the food, we can perhaps take it as an evening snack. That's a great idea. It's clearly a solvable problem. And second, they suggested that we want the menu to be published a week ahead of time so we can vote what we like and what we don't like. So that way we can bring down the wastage of things that are not being liked by the students that's being served for no reason and we can avoid it from the get go that we are not serving the food that most students don't Prefer to consume. So that was a great idea and out of asking and it was through analysis. So we didn't have to sort of convince our um, mayor's office or uh, we didn't have to convince the school um, uh, administration and it was very informative insight that we could share not only with the public administration but also. Find uh, problem that they wanted to solve for.

Speaker B: Wow. Wow. As you speak I was thinking of other use cases where you can actually apply this too. But uh, I'm falling short of the use cases. Where can we apply to? Not the big fat Indian weddings. Definitely not. But yeah, but I agree with you. I think food wastage happens um, because of not being able to preempt the amount that's going to be consumed. Right. So if I. I love the place where you're saying that you've looked at it before even food started getting made and you know putting. Applying those data and analytics there. Amazing Anusha, amazing. That's really, really great. Um, so moving along Anusha, we would also like to speak about um, I think you've focused on safety uh, of women in digital space. I see a lot of women writing on LinkedIn, many other platforms that you know, hey, this is not a place where you need to ask me out on a date or uh, you know, this is a professional site and so on, so forth. Or being attacked as well personally.

Speaker A: Right.

Speaker B: This may not just be about a date, but being personally attacked. So on this subject, why did you even begin focusing on this subject if I may ask?

Speaker A: See Rupa, I'm a proud mother of a nine year old. And um, we women, if we don't understand our issues, we cannot all. For the problems that we women face can be not only the responsible stakeholder for the problems that we face as women, we also need to bring the right kind of uh, thought process in thinking this through this problem. Because the world is not designed for women. Not yet. M. If we want a better world, we want to design the better world for women the way we want be feeling safe. Yeah. The safety for women, um, in digital space. This is a new uh, sort of a side project or I would not say it's a side project. This is a wonderful initiative that uh, some of my colleagues in the UN system, especially colleagues from UN Women, I want to definitely give a big shout out to my friends, uh, Andrea from un, uh, Women Mexico and also my policy trend, Lizette from UN Women here. Um, so we have thought or mulled over this topic. Yes, we initially during the pandemic we think about public safety for women during the lockdown, women had the most sort of vulnerable set of population. Women and children. Yeah, women and children during the lockdown didn't find the public safe spaces to be safe, uh, during the lockdown to be venturing out. So we realized that, um, solving for a problem, especially in New York City or in Paris, we did a thinkathon in London, Paris, Singapore. We realized that we are not only looking at the problem from a women's perspective. We realized that all the women across the board, doesn't matter whether they are from New York, London or from Singapore, had the same underlying problem of being safe. Uh, you know, the safety is pretty much in the front and center of every woman and they all look at the safety differently. Uh, and when it comes to digital space, when we look at the social media, the inclusive, uh, sort of things that happens in the digital space, to start defining what are those definitions of violence against women, what is that is considered as appropriate, which is not. How can we use technologies to not only, uh, have this unsafe space, if we want to use technology to create platforms like social media like Twitter, Facebook or Instagram, we should be using the technology to protect us too. Why not? Like, you know, how can we use uh, technology to not only identify these violence against women, gender based, uh, sort of discrimination that happens in social, uh, media platform, how do we want to trace them, track them, and alert on certain things that can be alerted to the right stakeholders. Right. Like, you know, the algorithm that we have built in collaboration with uh, UCB in Valencia and with our colleagues in Un Delco is that how can we alert on content that gets, um, published in social media channels like Facebook, Instagram and Twitter. And how do we want to make sure that uh, this voice of the women is being heard? Right? Like, you know, only when you alert it, uh, through numbers. 34% of women get abused every day on Twitter.

Speaker B: Wow.

Speaker A: And this is the only analysis that we did only on Spanish content. We haven't ventured into English yet. Just by looking at Spanish content in Twitter, we were able to identify the different kind of, uh, violence that is being portrayed, uh, or being shared in social media channels. Not only tracing them, we are able to alert to say that, hey, this is appropriate content. This is not giving the opportunity for women to seek consent to publish. Like, you know, you are giving the right for the woman to own the right of what she prefers to be published on her identity. This is not, you know, how do we bring that consent to the table? How do we Bring the rights of women that they have the rights to choose to publish. Yeah. So how can we bring that voices and the, uh, force around? How, uh, the digital case can be safe is the topic that we are focused on. And this is a passion topic for me. If you ask me to talk about this topic, I would perhaps need another podcast episode. Rupa. Uh, perhaps in future, once we have implemented this, um, technology in Guadalajara and Mexico, I can elaborate more, uh, and share some insights on what worked well in this particular use case. Yeah,

Speaker B: kudos to you guys for even thinking of it. I love the part when you say the world's not designed for women, but that, uh, doesn't stop you from designing it for yourself. Very powerful, very strong statement that you just said so on that thought and on the conversation that we had in Delhi. So I'm just going to poke your attention to women in tech or women in, uh, data sciences. Uh, and you did talk about, uh, um, biases in data. And I get asked this question all the time because I'm a woman on the other side and I run a company. And I'm sure you can ask this that, um, hey, so it's wonderful for you to be where you are. Have you faced any form of bias, um, or has there been a ceiling? Or how do other women break the ceiling?

Speaker A: Right.

Speaker B: So what are your thoughts around that?

Speaker A: I, um, think, um, there are two ways to tackle this particular, um, problem. Right. Like addressing tech and data. Uh, how do we want to not only bring people along, uh, how do we want to also make sure that we create that level of awareness that has been missing for a period of time? Right. Like, you know, it's not the, um, problem of not having women. It's the problem of not having enough awareness of what kind of career opportunities that women have, what kind of possibilities, or the possibility of where women can play a big role when it comes to women in data or women in tech. Right. Um, so my thought process, uh, the way I see, uh, this particular, uh, women in data and getting women interested in STEM careers is that one, we want to bring women along where we go. Yeah. How do we do that? Like, you know, perhaps in the, uh, internship that we offer, we want to make sure that equal represent 50, 50 of applicants who apply for internships are, uh, given equal opportunities at the interview stage itself. Right. Uh, making that as, uh, easy to go entry point. Having internship for women in data. Especially, for example, at unicc, we make sure that when we are interviewing for candidates, we have 50% representation from women and also 50% otherwise we don't even go into the process of interview. Right. Like you know um, bringing the opportunity for women from get go all the way from hiring for an intern to all uh, to the senior leadership needs to be thought through. Hiring process needs to be thought differently. And uh, these guardrails needs to become more from a guard to a guide rails because some of the corporate organizations don't have that kind of a mandate. But this is a mandate, it will become more of a guidance. Kind of presents a different kind of opportunity for women to also come. Right. And ah, the second uh, when we engage with our caption projects or the things that we do with our academic collaboration and when we connect we make sure on all the panel events we have equal representation of women. Otherwise we don't participate in these panel events and we also engage with projects with academia or our uh, uh collaborations with. We request for them to make sure that they bring women to the front and center and also to the table before we even sign for a partnership or even engage with them. See some of these things. If we don't do it, we women don't bring this as a factor to consider. It's never going to impact uh, in the long run. Right. And it's not sustainable either because once we make it a rule based uh, approach uh, it's not sustainable because who monitors this rule? Who kind of uh, pays attention to this? Who kind of will sustain it over time. It needs to be driven by the women for the women. I completely agree. And also we need to engage with the stakeholders at the right endpoints. Right. Like you know hiring process needs to be thought through and uh, academic institutions need to also make it easy on women to, to apply uh, for these STEM programs. Because the awareness of these STEM programs and the career opportunities they offer is not very familiar with the women uh, sort of within any society. Yeah. So uh, that's how I see this is my perspective.

Speaker B: Great. As usual. Anusha, very straight to the point. So I wanted to also understand in terms of if women want to join uh a public sector like yourself. So.

Speaker A: Yeah.

Speaker B: And want to be part of. Doesn't matter what they want to be part of data sciences or uh, you know maybe analysis or even on the engineering side. But what do they need to do if you can give them a bit of um, you know, maybe a framework.

Speaker A: See um, I believe um, in sort of I'm a problem solver in mindset. Yeah. So take up any and all opportunities that's available to solve real World projects, right? Like you know, whether it is internship or volunteering, uh, or with UN Digital co ops and other organizations, right. Like you know, there's a lot of public data available. Um, so I recommend anybody who's wanting a career in data or in tech and public sector, develop a portfolio. What I mean by portfolio is learn to tell stories through data, uh, um, perhaps review some tableau public dashboards, publish your content in social media. Social media is a very much an engaging platform, um, and also engage with new technologies ahead of time. Talk about it, right? For example, you have AI, you have tested something and you found something to be sort of applicable or interesting. Talk about it, right? Like you know those are. These platforms never existed in the past. Now the whole plethora of opportunities, um, that is one way to look at it. And most of my um, projects that I worked on, um, is ah, sort of a real world project that actually ended me landing up the next job or the next role. So when you volunteer and take up your passion project, uh, whether it is a real world project or a world project, uh, I think um, when you, when you pick what you want, I think opportunities will show up.

Speaker B: That's excellent. Anusha. So um, that's beautifully said. So people who want to join your team, if you're hiring, we leave your handles on the podcast for them to write to you. I love the way that you said that even with uh, data you can actually create a story that's a new one and I'm going to put that out, uh, there for people to know that that's a new one because people don't craft stories out of data. Right. But you so beautifully said that you can do that. Anusha. On a lighter subject, on a lighter note, um, you know you, you did say that you think from a data perspective, which, or you know, you're a uh, problem solver, rational thinker, so use a lot of your left brain. But I did see that you'd done a design course which is, on which is more right brain.

Speaker A: Right.

Speaker B: So the interior design, design course. So how did you heal on that from the left side to the right side of your brain? How did you happen to get through that?

Speaker A: Oh, by the way, um, I'm like everybody else. Yeah. So I went through the uh, uh, period of time in my career, uh, that I didn't have the work permit to uh, uh, work here in the States. So I ended up uh, for a course wanted to do, but I didn't have sort of perhaps the time for it. Um, so I'm a kind of person. When I have time, I sign up for courses. Right now I'm also going through two, three courses in the background. Because I'm a constant learner, I like to learn, uh, and sort of keep my um, emotional part and also my rational part nourished. Uh, Rupa. But yes, I went through a program, uh, which is a diploma program in interior design at Parsons School. Uh, that was one that summer that I spent here in Parsons. And uh, I believe in designing spaces that are conducive for um, mind, body and soul. So, uh, that kind of experience definitely helped me, um, nurture my uh, uh, what do you call the emotional side of.

Speaker B: That's really nice. I'm sorry I picked on that specific one. But Anusha, I found that to be fairly interesting. When I saw the entire data set about Anusha, it was all about, you know, being practical, problem solver, so the right brain. I was just super curious. But what an inspiration, Anusha, that you've been, I'm sure, to a lot of women who are listening to this or to anybody, right, Any, any potential who's listening to this podcast. I'm sure they realize that there's so much to do out there and you've actually proven that through some of the uh, pieces that you picked up as projects. I love the way you call some of them pet projects, but these are really meaningful ones that you're working on. I hope more people get inspired with the kind of work that you're doing. I'm so happy to have you on our show. I would love, uh, for you to uh, have any. Do you have any last words that you want to say before we wrap the show?

Speaker A: Um, I think, uh, um, uh, most of the topics that we focused on and um, what you did, ah, um, and ah, with a good heart and the intent being pure. Um, I, um, think uh, the time, energy and the resources are the three variables that you can control and uh, how you kind of channel it is how your career journey moves. So that's how I see it. Uh, Ms. Rupa. So thank you so much for this wonderful opportunity. Um, hopefully, um, my uh, journey was um, informative enough and uh, if anybody who's listening to this wants to reach out to me, please reach me on LinkedIn.

Speaker B: Awesome. Anisha was extremely meaningful. Thank you so much for this lovely conversation.