Shovon Sengupta
Director - Data Science & Artificial Intelligence (Ai-coe) @Fidelity Investments
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WORK HISTORY
Director - Data Science & Artificial Intelligence (Ai-coe) @Fidelity Investments
Boston, MA, US
Generative AI- Graph Machine Learning- Reinforcement Learning for Recommender System- Large Language Model for document QA- Explainability of deep learning models- Fairness and Trustable AI Models- Causal Pattern Mining and Causal Inference- Time Series Analysis using Machine Learning and Deep Learning methods- LLMs for drift detection
EDUCATION
Birla Institute of Technology and Science, Pilani
Doctor of Philosophy - PhD, Advanced Econometrics, Machine Learning & Finance (Financial Time Series Analysis)
The University of Texas at Austin
Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications, Artificial Intelligence
University of Calcutta
M.Sc - Master Of Science, Advanced Econometrics, statistics and Economics
ABOUT SHOVON SENGUPTA
With over 17 years of experience in analytics, AI, and data science, I specialize in predictive modeling, machine learning, natural language processing, and deep learning — with a particular focus on building production-ready solutions across risk, fraud, marketing, BFSI, and NBFC domains.My work spans recommender systems leveraging generative AI, graph machine learning, and reinforcement learning, alongside deep expertise in causal inference and causal pattern mining. I have also been actively engaged in exploring the reasoning capabilities of Large Language Models, fine-tuning LLMs for domain-specific applications, and applying foundation models to time series forecasting — areas where transfer learning and pre-trained architectures are reshaping traditional approaches.A central thread in my research is the development of fair, transparent, and trustworthy AI — specifically, the explainability of deep learning models used to inform high-stakes business decisions. I believe rigorous model interpretability isn\'t optional; it\'s foundational to responsible deployment.More recently, my work has extended to leveraging generative AI and LLMs for drift detection and document-based question answering, where these models show considerable promise when grounded in sound methodological practice.
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