Computing’s Top 30: Praveen Gupta Sanka

Praveen Gupta Sanka is one of our "Computing's Top 30 Early Career Professionals" for 2025. This program seeks to highlight an esteemed group of rising stars who earned this honor for their exceptional early-career achievements and role in driving advancements across the computing landscape. 

Introduction

My name is Praveen Gupta Sanka, and I am a Data Scientist at Meta, where I help shape product strategy using data-driven insights. My work sits at the intersection of applied machine learning, experimentation, and product decision-making. Over the past decade, I’ve had the opportunity to design, build, and optimize analytics and ML systems across domains including payments and advertising.

A significant part of my role involves balancing statistical rigor, engineering scalability, and real-world business impact. I build measurement frameworks, design experimentation strategies, and develop prioritization approaches grounded in cost–benefit analysis. I particularly enjoy translating analytical thinking into practical solutions that enable teams to make confident, data-informed decisions.

Prior to Meta, I spent about five years at Amazon, where I built a strong foundation in statistical reasoning and structured problem solving while working on advertising and payment systems. Across these experiences, I’ve consistently been drawn to solving ambiguous business challenges using data science and analytics. Beyond my industry work, I remain actively engaged in the research and professional community through reviewing, mentoring, and volunteering. I view technology as a continuously evolving discipline where learning, contribution, and collaboration are essential.

What inspired you to pursue a career in technology?

My inspiration to pursue technology stems from a deep interest in mathematics, programming combined with the motivation of creating tangible real-world impact. I have always been drawn to challenges that require logical reasoning and analytical thinking, particularly when abstract concepts can be put to work in real situations.

I am particularly fascinated by the ability to use data to uncover patterns, model uncertainty, and anticipate outcomes. Using past data to guide future decisions felt both disciplined and practical at the same time. That combination of analytical depth and real-world impact ultimately solidified my commitment to building a career in technology.

I found real satisfaction in applying these ideas to practical challenges. Building systems that directly influence products and customer experiences made the impact of my work visible and concrete. Seeing how thoughtful data driven analysis could shape real decisions reinforced my commitment to technology. In my experience, I noticed that technology is most powerful when rigorous thinking translates into decisions that affect people at scale. That realization continues to motivate me to build solutions that are not only technically sound, but also impactful.

How are you currently involved in the tech community aside from your job?

Community engagement has always been an important part of my professional journey. Outside my primary role, I actively contribute through peer reviewing, mentoring, and volunteering.

I have served as a reviewer for multiple international conferences and journals spanning machine learning, data science, and applied analytics. Peer review is particularly meaningful to me because it helps strengthen research quality, encourages clarity, and exposes me to emerging ideas and diverse perspectives across the field.

I am also actively involved in mentoring and knowledge sharing, guiding early-career professionals and supporting students. In addition, I engage with the academic community as a guest speaker and panelist. I recently participated in a panel discussion at the University of Washington Bothell, where we discussed careers in data science and transitioning from academia to industry. I also enjoy mentoring student teams at university hackathons, helping them refine ideas and think through how to translate concepts into viable products.

Additionally, I serve on a Technical Advisory Board at University of Washington Bothell, where I contribute industry perspectives on analytics, machine learning, and emerging technology trends. Within IEEE and the American Statistical Association (ASA), I have also participated in volunteering and organizational activities, including supporting events, contributing to committees, and engaging in technical discussions. These engagements allow me to give back to the community while staying closely connected to evolving academic and industry conversations.

Is there any emerging technology or industry segment you find exciting or interesting?

One area I find particularly exciting is the evolution of AI as a collaborative cognitive partner rather than simply a predictive tool. Advances in generative AI and AI agent frameworks are reshaping how we approach problem solving, creativity, and knowledge work.

AI is increasingly becoming an exceptional brainstorming partner, helping structure ambiguous thoughts, generate alternative perspectives, challenge assumptions, and refine ideas with remarkable speed. What makes modern AI systems especially powerful is how quickly they begin adding value, often requiring minimal onboarding compared to traditional tools.

Beyond ideation, AI also has the potential to reduce friction in everyday workflows helping the users to be more productive and efficient. By fluidly supporting activities such as writing, analysis, coding, and design, it enables a more continuous cognitive flow. From a broader perspective, AI is helping create a more leveled playing field by bridging skill gaps, whether through improving writing, accelerating development, or assisting with technical exploration. As long as individuals bring intent, curiosity, and the drive to learn, AI can act as a powerful enabler.

From a research standpoint, I am particularly fascinated by multi-agent AI architectures. Understanding when such coordination enhances reliability versus when it introduces unnecessary complexity is an important and emerging question. Overall, AI is shifting from being a narrow automation tool to becoming a general-purpose amplifier of human thinking.

What advice would you give to young professionals or recent graduates who are trying to enter your field?

Be curious. A genuine desire to learn, explore, and ask questions is one of the most valuable traits you can cultivate early in your career. Curiosity drives deeper understanding, helps you adapt to change, and often leads to unexpected opportunities.

Build strong fundamentals. While technologies and tools evolve rapidly, core principles in mathematics, statistics, algorithms, and problem decomposition remain remarkably stable. A solid foundation enables you to move confidently across domains and emerging technologies.

Think critically about problems, not just tools. It is easy to become overly focused on specific programming languages or platforms, but long-term success comes from the ability to frame problems clearly, evaluate trade-offs, and reason about solutions.

Embrace ambiguity. Many of the most impactful challenges in data science and machine learning are not clearly defined at the outset. Progress often depends on asking better questions, identifying assumptions, and iterating thoughtfully rather than waiting for perfect clarity.

Make Connections

Find Praveen Gupta Sanka on LinkedIn

Launch Your Career

IEEE Computer Society offers many resources for early career professionals. Don’t know where to start? Check out our page on launching your career in computing.