Her Story
About Priyambada
My journey in data science began in childhood, inspired by my father who was an assistant professor. His journey motivated me to continue learning about data science, mathematics, and understanding how things work fundamentally through science. I'm a builder at heart and love creating things, which led me to pursue my bachelor's degree in computer science. During my senior year, I discovered that data science was my path because I envisioned how it could empower people - this was when 'data is the new oil' was becoming a concept about 10 years ago. I wanted to understand not just how algorithms work and mine data, but the mathematics and statistics behind them, how to collect and manipulate data, and how to build my own algorithms. This motivation led me to a master's program where I became passionate about using data mining algorithms for social mining and understanding how communities are formed. I reached out to Christina Luhrmann, a well-renowned professor from USC working in this domain, and she hired me as a research assistant. For two years, I worked with her on an NIH project building a network and knowledge graph for biomedical practitioners who wanted to use machine learning, data science, and AI with their own data. After my research background, I wanted to apply what I learned in academics to the professional world. I joined BlackRock when they were forming their AI Labs research team - perfect timing for me. I started as an analyst in data science in 2018, was promoted to associate (Data Scientist 2) in 2021, and based on my contributions and projects, I was promoted to VP, Senior Data Scientist and Lead Data Scientist in 2024. I'm proud of several achievements, including going into a highly manual business domain around lending securities and building the first machine learning algorithm that got approved through SEC, which helped reduce time and increased revenue from X to 3X. I also built an internal tool using small language models and embeddings to help people understand career progression, skill development, and pay parity across different locations - a recommendation engine that helps employees upskill and get incentivized for doing the right things.
Her Interview
Ten minutes with Priyambada
01What advice would you give to young women entering your industry?
I have two pieces of advice. First, keep learning and always be a student. You cannot grow in your role just because you've learned some things and want to take them forward as the foundation. The foundation is the foundation, but you need to keep learning each day about your environment, about the technologies you're passionate about, and about people. Those are the three skills that should always be there in terms of learning. Second, lead in your own capacity. I understand that women particularly have that glass ceiling to break around, but if people lead in their own way, in their own journeys, with EQ, IQ, and what I like to call DQ, which is decency quotient, and you start with this commonplace, you move forward by learning each day new things. You share these new things with people who are your junior, who are your senior. That shows your leadership skill. It doesn't matter what position you are on, but learning and sharing are the two great skill sets that I would recommend for the future generation.
02What are the biggest challenges or opportunities in your field right now?
A lot of industries are getting disrupted by the democratization of AI, and this is a constant that I see continuing in the next few years as a constant learning curve for people in my industry particularly. The key is understanding the problem that you are trying to solve as clearly as possible - the granularity of that problem would help build solutions which are more robust and future-proof. People who are passionate about developing things should keep themselves engaged in the problem first. I particularly feel the focus is more on solving these days, and how quickly you can solve it, but if we can step back a little bit and focus on the problem first, asking is the problem even valuable to solve, that would be something that is a challenge right now. People are coming up with solutions and it's easy for them to come up with solutions, but is it scalable? Do you need to solve this problem? These key challenges are still happening, and people who have robust knowledge here will definitely play as a differentiator in this capacity.
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