Nilesh Patil

Nilesh Patil

Head of AI at DreamStreet. Previously Head of Applied Research, Dream11.

CV

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Profile

AI leader building deployable AI systems, agentic workflows, and organizational adoption in regulated and large-scale environments. Currently Head of AI at DreamStreet, building compliance-aware AI architecture for investor and trader workflows across research, brokerage, and advisory domains. Previously led applied AI research at Dream Sports / Dream11, including a Columbia University research center collaboration, cross-geography teams across India and New York, and production ML systems at 250M+ user scale.

Particularly interested in AI harness design, developer productivity, and turning emerging model capabilities into reliable workflows and products.

Education

  • M.S. in Data Science, University of Rochester — Rochester, NY — 2017
  • Bachelor of Technology, Indian Institute of Technology, Roorkee — Roorkee, IN — 2013

Experience

Head of AI — DreamStreet — Mumbai — 2026 — Present

  • Built full-stack AI systems for Indian investors and traders spanning research, brokerage, and advisory workflows in a SEBI-regulated environment.
  • Led development of a compliance-aware AI harness for brokerage and advisory use cases, emphasizing reliability, controllability, and audit-friendly workflow design.
  • Translated domain, product, and regulatory requirements into deployable AI architecture, agentic workflows, and user-facing copilots.
  • Drove AI adoption across the organization through training, rapid prototypes, and redesign of existing workflows around agents and model-assisted operations.
  • Built Hermes-based in-house automation agents across Marketing, Finance, IT, Tech, and HR, and set up self-hosted SLMs and agent tooling across local, GCP, and AWS environments.

Senior Principal Research Scientist / Head of Applied Research — Dream11 — Mumbai — 2019 — 2026

  • Built Dream Sports’ collaboration with Columbia University, NY and helped establish a multi-million-dollar research center focused on ML, AI research, and real-world applications.
  • Headed applied AI research for Dream Sports and led a high-performing cross-continent team of research scientists, applied scientists, and ML engineers across India and New York.
  • Led cross-team workshops to identify ~23 technical problems; converted 10 into funded projects over two years spanning sports robotics, LLM-based persona simulators, and agentic evaluators for personalization.
  • Delivered regular data, ML, and AI training sessions across Dream Sports for audiences ranging from ~10 to 200 participants.
  • Co-led Sports x AI sessions at Columbia University for students, post-docs, and faculty, translating industry problems into research and teaching material.
  • Designed and built a deep-learning churn prediction system and LLM-based behavior simulation workflows for 250M+ user lifetime trajectories.
  • Conceptualized and led real-time forecasting for ~50k+ forecasts under strict latency constraints.
  • Led distributed recommendation, content tagging, text similarity search for ~100M entities, and feature-store systems supporting 250M+ users.

Staff Data Scientist — Center for Vaccine Biology, University of Rochester — Rochester, NY — 2017 — 2019

Built automated and self-serve ML systems for bio-imaging research, including 3D reconstruction from hyper-spectral microscopic scans.

Data Scientist — AXA Insurance — Pune — 2014 — 2016

Built mortality-forecasting system and scaled statistical analysis pipelines with Spark and Python.

Data Analyst — AbsolutData Research & Analytics — Gurgaon — 2013 — 2014

Built a multi-stage equipment-failure prediction ML system using sensor, oil-tests, and human-labeled alert data.

Selected publications

For the full and continuously-updated list, see Publications or Google Scholar.

Dream11 era (2019 — 2026)

  • Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts. 2026, Association for Computational Linguistics, USA. openreview.net/forum?id=rLisRb1T1Y
  • Early Churn Prediction from Large Scale User-Product Interaction Time Series. 2023 International Conference on Machine Learning and Applications (ICMLA), IEEE, 2023. doi.org/10.1109/ICMLA58977.2023.00314
  • Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning. Proceedings of CODS-COMAD ‘24, ACM, 284–291. doi.org/10.1145/3703323.3703743
  • 6+ additional team publications in causal ML, recommender systems, and LLM applications — see Google Scholar for the live list.

Rochester era (2016 — 2019)

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