Senior Data & Applied Scientist
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Microsoft Advertising
Sep 2024 – Present
- Own the forecasting models that drive automated-bidding decisions across the entire marketplace — a foundational product contribution powering advertiser spend decisions at scale.
- Drive bid-recommendation and performance-estimation pipelines at the core of the automated-bidding stack — influencing ~$15M in annual advertiser spend.
- Lead novel research combining foundation-model forecasts with model predictive control to make automated bidding more adaptive and efficient — targeting publication at a top ML venue.
- Established CI/CD and security engineering for the team's ML codebase — automated testing and validation on every change; identified and resolved critical dependency vulnerabilities that could have impacted production.
Data & Applied Scientist 2
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Microsoft Advertising
Sep 2020 – Aug 2024
- Published SigIS (Simulator-Guided Importance Sampling) as first-author oral at ACM RecSys CONSEQUENCES 2024 — a new method for tuning large-scale recommenders by combining simulation with importance sampling. Co-authored companion MixtureIS paper at the same venue.
- Owned and modernized the production click-calibration ML stack for 4+ consecutive years, evolving it across generations (Probit → Random Forest → ONNX → full MLOps) and designing a generalized feature-extraction architecture that became the platform-wide default.
- Launched and grew an auction-simulation product line from research prototype to production as primary owner — reached $1M+ annual advertiser spend influence in the first 7 months post-launch.
- Brought Causal Transfer Random Forest (CTRF) from research paper to production click-prediction pipelines; co-presented the production integration at a Microsoft Research seminar with the original authors.
- Mentored 3 summer research interns over 4 years — one project productionized into $400K annual savings, another converted into the peer-reviewed SigIS paper.
Data & Applied Scientist
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Microsoft Advertising
Sep 2019 – Sep 2020
- Delivered a stalled cross-team counterfactual-evaluation project within the first 3 months — ramping up a distributed big-data platform and a large C#/F# codebase from scratch.
- Built the transfer-learning infrastructure (local comparison environments, distributed training, hyperparameter optimization) that directly enabled the team's subsequent migration from the legacy click-prediction model to Random Forest in production.
Visiting Lecturer
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Northeastern University, Khoury College of Computer Sciences
2022 – 2024
- Taught Supervised Machine Learning (graduate-level, 2 offerings) and Data Analytics and Machine Learning II (undergraduate).
Researcher
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TCS Research Lab, Delhi
2014 – 2017
- Designed and built a Bayesian Network Inference Engine adopted across 20+ client organizations.
- Published 7 peer-reviewed papers (IEEE VIS, VAST, FUSION, ICDMW), won 4 international data challenges, and produced research output that contributed to 2 granted US patents.