Alumni Profiles Series: Sunith Suresh

 February 13, 2023

Sunith Suresh is a Manager of AI Solutions at C3 AI, where he leads cross-functional teams to scope and deliver high-value AI/ Machine Learning (ML) solutions for enterprise clients. Before that he was a Senior Data Scientist and early employee at Durham-based InfiniaML, where he developed and deployed AI/ML solutions for enterprise clients. He received his M.S in Statistical Science at Duke University in 2017. Previously, he worked as a senior financial analyst at a Boston-based private equity investment company. He completed his bachelor’s degree in Accounting and Finance from Indiana University in 2010 and then completed his CPA.

Tell us about your current job.

I engage with business leaders at enterprises, to identify and scope opportunities where AI/ML solutions can deliver substantial value for their business; then I lead cross-functional teams of data scientists, engineers and designers to configure and deploy the solution for my customers. It’s an exciting and rewarding role where I get to help drive my clients to actual business value from AI/ML. Coming from a data science background, I leverage my deep technical knowledge to guide my clients to success. But technology is only part of the solution; delivering business value involves ensuring the solution can scale in production, and end-users utilize the solution to achieve desired business outcomes. The key is to solve the right problem in the right way.

You’ve made a number of changes in your career, starting in finance and then returning to graduate school in statistics. What prompted you to make those transitions?

Interestingly, finance was one of the earliest adopters of machine learning with algorithmic trading back in the 1980s and 90s. They began to implement data-driven models to forecast stock prices, make investing decisions, optimize portfolios, and then continually re-adjust with feedback, which really is a full, end-to-end AI system. I came to study statistics at Duke with the intention of returning to finance in a quantitative role afterwards. At Duke, I saw how machine learning was being used to solve all kinds of problems from genetics to e-commerce recommendation engines, which compelled me to pursue my current path. Duke was really a pivotal change in my career. I was exposed to all these ideas ranging from Bayesian Statistics to algorithms and programming by amazing, brilliant professors–it was awesome!

What is your favorite thing about your current job?

The cool thing about my job is I get to wear several different hats. From a data science perspective, I learn my customers’ business problems and design an appropriate AI/ML solution. Then from a product perspective, I design application workflows, ensuring the business users can achieve desired business outcomes using the solution. Finally, from a leadership perspective, I create both a vision and a roadmap to engage and drive my clients towards success. The last part can be challenging: everyone has a different way of looking at the business problem and you have to help them stitch together the pieces to see the full picture, but it is also quite rewarding. Uniting a team of motivated individuals toward a common goal fundamentally creates business impact.

"Technology is only part of the solution; delivering business value involves ensuring the solution can scale in production, and end-users utilize the solution to achieve desired business outcomes. The key is to solve the right problem in the right way."

What advice would you share with current Duke graduate students who want to enter the field of ML/AI-related tech industry?

The most important thing is to discover and stay focused on your personal interest. AI/ML is a rapidly evolving field, and there are a fair number of opportunities. Let your interest and intuition help guide you to narrow down the search. Don't get too focused on the technical side, but rather think about the broad impact and value of the opportunity as well.

What is one of your favorite memories of Duke?

I loved hanging out at Monuts and watching basketball games with my friends. One of the best parts of my Duke years was all the cool people I met at Duke and the friendships we built!


Lan Luo, Ph.D. Candidate in Neurobiology
Lan Luo

Ph.D. candidate, Neurobiology

Lan Luo is a fourth-year computational neuroscience Ph.D. candidate in Neurobiology currently studying how to decode visual input from neural data while the brain activity fluctuates with input history. Passionate about machine learning & data science application in the intersection of neuroscience and machine learning, they aspire to apply state-of-the-art methods in computer science to solve computational neuroscience questions and to improve computer vision with principles employed by the biological neural networks.