How a former Astrophysicist is trailblazing a career in Data Science

I would love to start broad, tell me a little bit about who you are, how you came to be where you are now, and specifically, where/ when did your passion for technology start?

I don't remember exactly when this passion really began, but I definitely connect it with my love for science, especially physics, and math. From quite a young age, I loved how physics mapped to math; and how, operating in the math world, there could be new equations and relationships I could derive that then, when mapped back to physics, would indicate "discoveries" about phenomena in our world. This got me excited and inspired to learn more and more. As I continued, I soon reached a point where there was not much that could be done with pencil and paper: very few phenomena in the world have "analytical", "closed-form" descriptions/solutions. That’s when I began really exploring technology as the way to implement approaches to study these more complex phenomena & systems (e.g. by simulating their behavior).

As for who I am, I was born and raised in a little village in the northwestern part of Greece. My mom and dad never finished grade school. The last thing that they had expected was a little girl interested in physics and math who would end up jumping on a plane and coming across the Atlantic to get a PhD. Even if my family's background could not have predicted my path, my sense of curiosity, that has driven me through my career, was most certainly seeded while I was growing up. And, since then, as I often say, I've been suffering from a lot of curiosity!

What kind of work are you doing now? What are the main emerging areas you’re seeing in Data Science and Financial Services?

It goes without saying that there is a lot of excitement around Generative AI, which I think has enormous potential. There has also been a lot of misunderstanding around what exactly Gen AI is and can do. I've made it my mission to help people disambiguate so that we appreciate it for what it is good for and use it properly. Beyond that, I always try to have a balanced portfolio of things I want to do and focus on. While Generative AI has taken up a lot of the conversation, there are so many other interesting and useful technologies emerging. Naturally, another area of focus in financial services is investment signals built on data insights that can help us inform better investments.

What advice would you give people who want to get into Data Science?

First off, curiosity is the most important prerequisite for a successful career in data science. Genuine curiosity. Second, I expect my data scientists to think scientifically- we are doing science after all. I’d also encourage people to make sure that they really understand the domain they’re working in. When I started, I knew nothing about finance. It took me a solid six months to learn (and still learning), but it is imperative that you do learn. Sometimes organizations keep their data science practice separate because they think of it as a technical thing, but there is no possible way to deliver the best solution to a problem in a vacuum. Make sure you're interested in the domain where you choose to work as a data scientist so that the curiosity and desire to learn about it comes effortlessly; that's how you will really understand how to apply data science to the domain and deliver maximum impact.

Also, make sure you know your math really well. Your statistics, your models. Nowadays it's relatively easy to go online and find pieces of code for everything (or ask Gen AI to produce it!); and, it's not rocket science to take pieces of code and stitch them, etc. But that's not what data science is. The science often is in knowing the one out of 100 times that standard code is not going to work for your problem. You can really only know that by genuinely understanding the math, the statistics, the algorithms and how they work. Coding at the end of the day is the means to an end but you need to know how to arrive at that end.

What do you think needs to happen to get more women and underrepresented groups to technology leadership like yourself?

Firstly, we have to make sure women in tech leadership are visible and shared with the world. Not that there is any doubt we exist, but sometimes we are consumed in our careers trying to do the best we can do for the job we have. From our standpoint, it’s important that we share our experiences and for others, to give us opportunities to do so.

That said, I do have a fondness for an idea that I haven't had time yet to put into action. I really think that we need to start exposure to technology, science, and math as early and young as possible. We need girls to know that they have nothing to be intimidated by, they can choose whatever path and with no conflict with whatever level of femininity they choose to have or not. They can exist just as they are. We need to normalize all of this as young as possible so that children aren’t impacted by any stereotypes being pushed towards them. It shouldn't even be a big deal, there shouldn’t be any thinking around, “oh, can I do this?” When a child grows up like that, the world will indeed be their oyster.

I see you’re super passionate about Gen AI, what impact do you think Generative AI will have on the financial services industry? What risks and opportunities will it bring?

There are a lot of opportunities with Gen AI. It goes without saying that anything that can help you create or consume content faster can change the world. Why? Because it frees up all this additional time. That said, I don’t see this as transformational just from a productivity gains perspective.

I am actually sometimes frustrated when people focus just on the ability to do something faster and not also the ability to do it better. Automation is different from what we are talking about with (Gen) AI. Why is that? Well, if I’m a finance professional that would benefit in the way I am doing my job by, say, consuming the content of ten documents a day, the odds are that I won't ever be able to actually read all ten of them. With Gen AI, I can synthesize all of the information in all ten. So it's not just that it could save me time, it's actually giving me access to additional, different information.

Once upon a time when thinking of information asymmetry- meaning some people having access to information that others didn't- what would come to mind is sending someone to X chain's store parking lot to count how many cars there are to infer what company X's sales will be. If you had the resources to have people do that, you knew more than others. Nowadays, information is more or less accessible to all of us at a relatively low cost. The "21st century" version of information asymmetry is about who can actually find, aggregate, synthesize and act on all of the correct and relevant information as fast as possible. Who gets to that information faster, and in a more comprehensive way? That's where the differentiation comes in. Generative AI will be a big tool in helping companies act fast on the data they have. 

So, I think generative AI is going to change the world indeed. That said, I'm not sure that this revolution will come as fast as we think. 

Who helped you rise to this role and how do you pay it forward?

I need to give an enormous shoutout to one of my most precious, helpful mentors. Her name is Professor Angela Olinto and she was one of my graduate advisors at the University of Chicago when I was doing my PhD in astrophysics. It was recently announced that she would become the provost at Columbia University and I am so very proud of her. The way I pay it forward is by trying to be as supportive as she was to me for others. I remember the first time she sent me to present at a conference. I was only like three months into my PhD but she gave me the confidence to just go and do it. I’m just hoping that one day I'll be as amazing as she is.

How do you hire? What do you look for in people?

In terms of hard skills, it's great if they already have a good math foundation. When you’ve been doing this for so many decades you really get to a point where you feel, it's no longer a thought or piece of knowledge but it's a feeling. You're saying “I know that's wrong- I don't know why yet but let me think about it and tell you.” In order to develop that deep connection, you need to practice for a long time. So the sooner they start on that, the better. But ultimately, I have to go back to that curiosity mindset. I look for people that genuinely, organically look around the world and have questions. I think that's the most important thing and it is something you cannot teach.

Meytier interviews leaders of all backgrounds about their journeys, their work, and their outlook for the future. Know someone who we should interview? Reach out to us here.

Read recent Leader Speak interviews

Paving the way for the next generation in tech, an interview with Dipali Trivedi

Becoming a CTO and paying it forward, an interview with Kathy Carter