Interviews with Leaders in Fintech & Web3
Interviews with Leaders in Fintech & Web3
Taking the Reins of Your Career: Graduate Advice from Chief Data & Analytics Officer at UNICC Anusha Dandapani
Anusha Dandapani, Chief Data & Analytics Officer of UNICC joins Ying Cao to discuss her exciting career journey and how she has navigated herself toward her passions.
Anusha Dandapani has had a long career in data science inspired by her passion to use it to solve real-world problems. She was the Data Science lead at Barclays where she led several strategic initiatives related to electronic surveillance and financial crime compliance. She is currently the Chief Data & Analytics Officer at United Nations International Computing Centre where she advises and partners with other UN organizations, Industry, and Academia on solving business problems by leveraging Artificial Intelligence, Machine Learning, Data Analytics, Data Engineering, and Data Science offerings and capabilities. She's passionate about educating young students and recently led an ideation workshop for students to apply data science to tackle food waste in schools.
In this exclusive interview, Anusha imparts upon us the wisdom she's gained throughout her career path, from starting off as a software programmer and working in the financial services industry, to immersing herself in a career of data science and scaling her skills and experiences to work on initiatives involving using data science to solve real-world problems. She shares with us what mindset we should embrace, whether we're starting out or in the middle of our careers, to successfully pursue our passions and challenge ourselves.
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[00:00:00] Ying: My name is Ying Cao. I'm a co-founder at Work in FinTech and today we are honored to have the amazing Anusha with us, for our podcast.
[00:00:15] Anusha: Hi everybody. My name is Anusha Dandapani I'm the Chief of Data and Analytics Officer at United Nations International Company.
[00:00:25] Ying: Awesome. And it's such a pleasure to have you with us today Anusha because I've known you for so many years, and I think your career journey and your vision about what's coming for the industry is always inspiring for the benefit of the audience.
[00:00:40] Can you quickly introduce yourself and maybe a little bit on your career journey, career path, and you mentioned that you were the Chief Data Officer at UNICC. So maybe it's helpful to describe a little bit how you landed this job.
[00:00:59] Anusha: My name is Anusha Dandapani, as I said, and I'm the Chief of Data and Analytics Services that we offer at United Nations International Computing Center.
[00:01:12] My career journey started very early in the years when my passion was about mathematics, statistics, and science at an early age. My curiosity for learning was around these three specific topics. And by default, I come from an Indian upbringing. So for me, it was more about where can I identify a career where I can make the best use of these three strengths of mine?
[00:01:46] So I ended up by default choosing computer science engineering as my career path. And what did happen is that I slowly realized computer science engineering definitely gives you all the sort of competence of my passion that I wanted to apply. However, I quickly realized that recommending and also understanding domain knowledge is necessary.
[00:02:16] If the domain acumen or understanding of the industry where you're going to apply the science and math or your engineering education to was necessary for my career, so my first job, I happened to be a software programmer and I programmed for the ATM to dispose of cash. It happened to be at a tiny community union bank and luck would have it is called United Nations Federal Credit Union Bank.
[00:02:48] So I started my career as a software programmer for an ATM to dispose cash and there you have it. That's how I learned more about the UN and the UN's organizational structure. But I quickly realized if I wanted to create a career in finance or financial services, I need an understanding of the financial elements that come along with when you're working in a bank.
[00:03:15] Though being at a community bank, I still had to understand the commercial banking aspects and I started to channel my learning and focus to understand more about the investment bank. Topics, especially around securities and derivatives, I'm a self-learner and I learned that these two topics definitely interest me.
[00:03:39] I ended up taking a certificate course for me to get certified in derivatives and securities that quickly sort of have accelerated my career and I was able to sort of find and land a job in an investment bank, Credit Suisse. This was way back in 2005 and six. And I started my career as a technology analyst for one of the investment bank in the trading platform.
[00:04:04] And over the years I realized that again, finance is an amazing sort of industry. For you to grow with sort of a clearly defined path for your growth. And I also went through a midlife crisis. I have to be harnessed in my career journey, right? Somewhere around seven to eight years in my financial services experience, I realized that I'm still missing something which happens to be sort of a key element.
[00:04:36] If you were to imagine three bubbles in a Venn diagram, right. Like, I had the domain expertise of understanding of the industry that I'm working in, but there was still that one element that was missing, which is sort of the passion for how you are looking to solve problems in this particular industry, not just the industry, but also how you are gaining this sort of Feel-good feeling by solving this problem.
[00:05:06] So I quickly realized that there is this lack of this element of understanding of data. And, uh, I decided to actually forego my career and decided to go through a full-time path. And I realized that the more I keep my job and also pursue an opportunity for me to learn about data science that will help me to not only learn.
[00:05:32] But also learn by doing at the day job that I was working at that moment. So that kind of gave me the capitalist or the accelerator for me to move from just being a financial specialist to a technology specialist to more around data science. So to have a career and it definitely helped me a lot by doing a program that gave me both the opportunity to work and also apply my data science a newly acquired skill.
[00:06:06] So at the last bank that I worked for, which is Barclay's. So that's where Ying and I were colleagues, wonderful experience in Barclays. I do feel that bringing data science into this industry is very much a necessary element. And also as a career, I wanted to make myself sort of open to see how this data science can bring us to a sort of problem-solving mindset.
[00:06:35] And then also a mindset wherein data-driven design is an approach for problem-solving. And I was able to quickly apply and learn over my years at Barclay's. And now what I have done is that now that I have learned the skill set and the framework of how to apply data science approaches to solve problems.
[00:06:56] I decided to take all those good learnings that I got and apply to a bigger sort of scale of problem-solving here at the UN. So as I joined UNICC, my role was sort of very much tailored for me to understand how we can drive problem-solving using data. And at this current role, my focus is also to see how I can address these challenges that we see in the humanitarian crisis.
[00:07:29] And what are the elements that these challenges can be approached with the business mindset using AI and mission learning and how can we apply these good technology toolsets to solve for problems at a scale that. It's bigger than myself. So this is a great milestone in my career. I might write knowing and thank you for giving me the opportunity to talk about it.
[00:07:59] Ying: That is awesome. Anousha thank you so much for sharing your story including the midlife crisis, which I'm sure a lot of us have gone through that phase. And I think a couple of things you share that seem really interesting.
[00:08:16] I think the first one you mentioned as you are moving your career into investment banking, you actually take a certification right, in derivatives and securities, and you find that extremely valuable to you, right? As you're pivoting your career. And the second thing you said is as you're doing the data science work, you really benefit from learning while doing so can you elaborate a little bit especially given many of our audience are students, young professionals who are looking to enter, say a stem field, right.
[00:08:55] Or data science and machine learning. What are the things you find can be really helpful, right? If you can give the advice to your younger self as they are looking to evolve their career and build their skills and knowledge in terms of learning opportunities, what are the best ways you find as you're acquiring those skills necessary to make you successful later on your career?
[00:09:24] Anusha: There are many things that I learned and then I also failed, so maybe I would talk about things I wish I did. Advice to my younger self, if you may want to put it that way is that I feel when you're young and when you are just out of graduate program from any of the universities that you're out of that long term thinking.
[00:10:00] When it comes to choosing your career, we all think that, okay, now that I have chosen a path, and this is the path that I'm going to continue to follow for the rest of my journey. I made that assumption and that assumption became sort of my own learning by itself that that assumption can be sort of clarified by the decisions that you have to make for your own self as to where is it that in your career journey that you want to get.
[00:10:33] That kind of clarity didn't come to me at an early stage. So I decided to use the traditional path of choosing computer science or financial services as the go-to career. But choosing to learn in the middle of my career journey kind of gave me a different lens to look at my career. So that is key learning that I got is to learn, learn continuously because learnings can take the assumptions away from you with more sort of clarity around where you want to go and the second advice I would share with an audience of it as a younger audience is yes, it doesn't matter the kind of industry you choose.
[00:11:19] It doesn't matter. The background that you come up with, whether it be the science, math, or computer science or financial services end of the day, if you have the problem-solving mindset, because the end of the day, it doesn't matter whether you work in the public sector or the private sector or the traditional corporate sector, you are going to be faced with multiple challenges day in and day out.
[00:11:44] Yeah. These challenges are problems. If you end up looking at these situations that have been posted at you as a problem that you are looking to solve for, right. Most problems are not easy problems, especially when it comes to real world problem-solving and we assume that, okay, now that I've solved the problem.
[00:12:05] I think I deserve this in my career and I need to move on to the next level. And this is where I need to be. Sometimes we kind of put ourselves into this time pressure that I want to get that at this time and this space and this sort of a journey, having that open mindset, because that career journey is each of its own.
[00:12:32] And the journey is the driver of your career journey is you and you have to define for yourself what success looks like that kind of takes the pressure away from you to succeed in a short period of time, or like, you know, move fast and break things, mindset, move fast and break things mindset needs to be applied in problem-solving and not defining very want to be in your career.
[00:13:00] Ying: Gotcha. Yeah. That's really good advice, right. I think especially for many of us who have worked for. You know, 15, 20 years now is the hindsight. It's really important to understand that your career is a journey and the journey itself, it's the destination because everything you have done or to your point that you failed actually become learning opportunities, right.To really set the foundation for your future success and you also mentioned passion, right? Especially as you mentioned that decision you took, as you're looking at UN to solve a problem that is bigger than yourself and really apply what you have learned into the bigger scale of the problem. And we often here, especially young professionals, right.
[00:13:56] Are talking about the trade-off between passion versus money, where versus status, and what is the learning you get by discovering that passion is really important. And then how do you envision, right? Or what difference do you realize by having a passion versus not having passion that has an impact on the longevity of your career.
[00:14:26] Anusha: Having a passion is very, very important. And I do feel you rightly put Ying to say that what is the balance between passion or making money? Right. My answer to the young generation would be you should do both. There are opportunities where you're working in a path career path wherein your money is the focus that is always a reason for you to keep your passion alive by working on passion project of yours.
[00:15:00] Like for example, when I was in for the past 15 years when I was in the corporate career helping people and also identifying platforms or opportunities for me to, how can I focus my skill and my time. On helping humanitarian crisis or human rights or inspiring women to choose a career in technology.
[00:15:24] These, all these three topics have been my passion topics, and nothing that I was doing as my day job was stopping me to work on these passion projects. And the good news about having passion projects is when the time is right, your passion project can become your career. So if you have three or four passion projects that you think that you always want to give a try, always start with it like yesterday, because you will never know some of your passion projects will turn out to be your long-lasting and most sort of fulfilling part of your career.
[00:16:02] So I do believe the younger generation's mindset is valid that they want to make money. Yes, there will always be a milestone that you can set for yourself to say, this is the money I'm happy with. I'm sort of like, you know, I have all the sort of necessary elements for me to sustain. And now is the time for me to pursue my passion.
[00:16:25] And that is something you can start as early as you graduate or as early as you are probably 30 years old or as early if you're 50 years old. So there is never a deadline, but it is more about how you see your passion project manifesting into your career. Yeah. So yes, passion-driven careers are long-lasting careers.
[00:16:48] They kind of give you the fulfillment that you need. And also bringing money to the table in this kind of world that we live in is also necessary, but you don't want to put too much emphasis on.
[00:17:03] Ying: That's really well said. And even taking myself as an example, right. Coaching was a passion project for me. Right. It was a set job until I was crystal clear. That's something that I probably want to dedicate myself to for a foreseeable future. Right. As a career, and that actually turned into a full-time career. Similar for you. Right. So I think it's really interesting. Especially when you said you can actually have both right.
[00:17:33] Both monetary success, as well as passion and the career that really driven by passion, is probably going to be long-lasting and the money will come as well as a natural byproduct of your passion realized.
[00:17:53] And you mentioned about UN right. And then there's a bigger problem, bigger skills, a problem to solve. Right. And you came from a finance background, right? As you said, you started as a software engineer and then worked in the investment bank, right. For more than a decade. How did you make the transition?
[00:18:14] And also, how do you feel like the challenge or some of the decision-making for you to make this switch in the middle of your career? And I think a lot of our audience has similar questions, right? Especially people who have worked in the industry for more than 10 or 15 years, the opportunity cost and also the cost for them to switch something mid-career it's really high.
[00:18:42] So many of them are a little bit conservative and also evaluating the opportunities in front of them. So what makes you make the leap? And then what types of opportunities do you see out there that make you want to take this risk?
[00:19:04] Anusha: This is a complex and very interesting questioning. You put me on spot on there for me to think through. But yes, it was not an easy choice or a decision for me in the middle of my journey to switch to something totally different from what I was used to. My thought process around the choice of career.
[00:19:33] And when do you switch and what kind of opportunity costs that we incur or the trade-offs, if you may, is that, Hey, end of the day, you have one life to live. If I'm going to give myself the permission to try and, if the worst outcome comes out of this experiment, I can always go back to the good experience that I have built over time.
[00:20:01] So that was my backup plan just think through, to say that, okay, I'm going to give myself, the permission to try. Yes. It is a big risk for me to switch from what I'm not used to, to doing something I'm might totally sort of not ready, but don't get me wrong. I had a wonderful journey in my previous experience in my financial sector and throughout my career.
[00:20:34] The work was fruitful, but also stressful because it's just a time consuming career, but having switched here to a private sector and to join an organization like the UN I do see that being a data science leader and serving a community like this comes with its own unique set of challenges too.
[00:20:59] There are seven parts of the organization or even part of the sector that itself that I'm working with are not really data-driven. They're also resident to adapting to any new technologies and approaches to solve the challenges also. Right? So there is always the situation where the business leaders who look at data or AI and ML as a silver bullet, they think that they're unaware of the challenges that come along with the silver bullet to solve the problems.
[00:21:35] Right. So often I do realize that me wearing multiple hats helped grow from where I was to where I am. And I do believe hats, like, you know, being a coach at some time, being a top leader, being a, go-to person for your various stakeholders, these are the intangibles that you learn from in a corporate or even a private-sector career.
[00:22:02] Right? Like, you know, those learnings definitely help you hone your leadership skills and your organizational skills, and those are the elements that you can carry forward when you switch industries. Yeah. So regardless of the industry, whether you're working in the private sector or in the public sector, the situations that you would be facing would be someone or somebody who's introducing you to the elements and challenges you will always have to find.
[00:22:37] This to maneuver and, and also ways to be this person who's influencing the decision-makers and also possibly drive strategic decisions. Those skill sets are muscle that you build as you walk through your career path. So that is the value that I bring to this industry that I learned by doing in the past life.
[00:23:08] However, don't get me wrong. There are challenges here too. There is a complexity to the understanding of the organization that comes along with the time. And I'm also part of an organization where people have been here for a very, very long time. And I'm probably just a small child who's trying to make changes in the work that I'm doing.
[00:23:31] So challenges are everywhere, that will always be the case, building a mindset to tackle these challenges is part of the career journey that you will learn,
[00:23:48] Ying: That's a really important learning outcome, right? Just to how you approach challenges because challenges itself doesn't necessarily mean bad, but you are to have to right level of mindset, right.
[00:24:03] To really take that as an opportunity and grow and reflect is probably what really sets us apart. So thank you so much for sharing that. And in terms of your current career at UN, what is your typical day look like? And then what are some of the interesting use cases that you guys leverage to the extent that you can share?
[00:24:29] because I understand a lot of the projects are probably confidential, that you think unleash this beautiful power of data science and then technology that you always felt so passionate about?
[00:24:47] Anusha: Yes. So most of the challenges or the problems that I'm currently focused on Ying is to first introduce the data-driven mindset within the organization.
[00:25:03] and how can we sort of bring forward this change of not just the executive leadership, but within the organization. We have different sorts of challenges the first thought process that I'm focused on bringing to the table is how can we use data for better services? Not just within the organization, because our focus is to serve the people on the planet and also bring forth this data-driven insight mindset of how to better understand what happens.
[00:25:43] Why it happened, what might happen next, and how to respond to that sort of situation? How can we make this data-driven is one of the use cases that I'm working on and then also better management and security of data. When I talk about the security of data, I'm referring to how to manage the sensitive data and the private data elements of, to the data that how do we
[00:26:10] share these good assets that we have in a safe and a secure way is one of the greatest challenge s that we continue to face and given that the problems that we are solving for the greater good, our mindset is about making sure that we are focused on cloud-first, and open-source strategies, wherein that
[00:26:38] our data and the approaches are replicable and also work for the economics of scale. And one of the performance indicators that we are really focused on how many of these use cases that we can help solve for this organization that can help us not only drive the data-driven mindset.
[00:27:06] But also like, you know, from delivering use cases for stay quarters based on the outcomes and the principles of the UN that we can achieve. So at this level, we are charging and also focused on use cases at the descriptive analytics level, which is essential to understand what happened. When did it happen?
[00:27:28] How did it happen? But we are also focused on use cases like, you know, advanced analytics of ad, and we focus on predictive and prescriptive analytics to identify how data can help us simulate or understand certain situations. Say for example, right now, if you take COVID pandemic, how can we better respond to this crisis?
[00:27:51] What are the elements that we can simulate to foresee and forecast? So we can respond better to these crisis situations is something that we are also focused on as a use case. And third biggest use case that I can share is that how we continue to work with data in silos and data being in silos is not very helpful for us to focus on a single source of trophe.
[00:28:23] figuring out opportunities for where in that we can bring in this transformative infrastructure where we can cross leverage data across the organization and create data and an analytics mindset and embedding it in decision-making is the front and center or the overarching goal of all the use cases that we are focused on.
[00:28:50] Cross-functional collaboration and figuring out ways to bring data in silos to one single source of truth is one of the biggest use cases that we are working on. It's a challenging use case that we are working on.
[00:29:06] Ying: Gotcha. Oh, that sounds really interesting. And you know, we are called Work in FinTech and our belief is
[00:29:16] every company and every organization is going to become a FinTech company or FinTech organization because finance and technologies are everywhere. And based on your experience, right, you worked at an investment bank focusing on data analytics and you now work for the UN and leverage data analytics and data sets.
[00:29:38] To solve worldwide problem. What is your definition of FinTech and what opportunities or challenges do you see in this broader sense of industry?
[00:29:52] Anusha: As you rightly said, being finance and technology is a thread in every industry and every organization. I do see that this thread or the thread in the fabric of every industry only growing further because the more we move into this digital world the digitization and the digital transformation will always be the front and center for all the industries that we're focused on.
[00:30:29] And how solution be the technology solution or financial product. How would they consider these ideas and thoughts and the solutions that will come through from this thread or FinTech is how inclusive is it? How sustainable are these solutions that we are co-creating, and how these solutions can be adapted?
[00:31:01] Will drive the most part of the growth and future and the younger generation. The more we see that they are more self-aware and more aware of the ecosystem or the environment that they are living in. How environmentally sustainable approaches are incorporated in FinTech products or FinTech solutions or platforms, or be it in the CIN FinTech industry.
[00:31:30] It's going to become the name of the game and it's going to become the front and center focus, be in the FinTech industry or be the technology industry itself. So I'm very optimistic and positive about it. As long as we consider that inclusive growth. It's a one-off not one as the core element in the growth of the FinTech industry.
[00:32:02] Ying: And as you rightly said, so the opportunity in the FinTech industry is only going to increase, and I know your team is expanding, you're hiring people, and then there's probably more problem that can be solved by data analysts. And thinking of skillsets, right. And also a personality fit right.
[00:32:24] For your organization or the needs that you see in the special arena of like UN an intersection with data and data science, what do you think are some of the key skillsets and then personality for, you know, a student or young professional, or even experienced professionals when they are considering a career in UNICC, especially in the data science arena.
[00:32:57] Anusha: We are constantly looking to expand our team or the family. So even if I may put it that way and we are definitely looking to bringing this gender balance in our team, and we are wondering how we can bring forward more women and more young women to the workforce, and also consider a data science and technology as one of their career path or the career journey to choose.
[00:33:31] That is something that we are always looking out for. And we look out for students or the young professionals who have this mindset for learning and curiosity in problem-solving and bringing their best self to work every day. These are the three top things that we look up in young professionals skills can always be acquired.
[00:34:01] It doesn't matter the industry be in the public sector or the private sector but these top three intangible skills we always look out for in young professionals that gives us and also the professional more of career journey mindset. And then I also believe in this remote world that we're all remotely working and we don't have many opportunities to work together as a team or collaborate as a team, having more opportunities to have mentors and having more opportunities for students.
[00:34:46] I identify to talk to somebody who's an example in that industry is something that I highly recommend for any student or a young professional before you choose a path because, that kind of one-on-one also experience sharing from one individual who's already been there, done that will help you hone your experience in the path or the organization that you would choose to work for.
[00:35:22] Ying: Awesome. Thank you. That is really encouraging because we have a lot of students ask us, do I really need to have a data science degree to work in a data science job? And I think exactly, as you said, the skills can always be acquired, but the mindset is actually the differentiator, not only for one job, right? Probably for the rest of your career journey.
[00:35:46] Anusha: Yes. And data science is the next big thing that everybody wants to be part of. Everybody can come along in the journey of data science and there are many ways to learn. And I highly recommend students and young professionals to continuously do. In the career journey for data science, there are many open-source, and also plenty of courses on Coursera, YouTube, that offer this fundamentals of data science.
[00:36:27] I highly recommend considering. See what this data science skillset is about and continuously learn, because what is relevant today is not going to be relevant in this data science community itself tomorrow. So if you're persistent and a continuous the data science path is yours.
[00:36:53] Ying: Awesome. Thank you so much Anusha for joining us today.
[00:36:58] Anusha: You're very welcome and thank you for this wonderful opportunity. And I can only share my, not just my challenges, but also my failure of where my blind spots are. And I'm very thankful for this opportunity and I'm looking forward to hear more success stories.
[00:37:20] Ying: definitely. Awesome. Thank you.
[00:37:23] Anusha: Thank you.