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ML Research Scientist
Machine learning research scientist specializing in generative AI, LLM reasoning, uncertainty quantification for deep models, and sequential decision-making. At Netflix, Spotify, Princeton, and Columbia, I have advanced techniques for reasoning, structured event modeling, calibration and Bayesian uncertainty for large networks, offline reinforcement learning, and exploration. My work appears in NeurIPS, ICML, AISTATS, Machine Learning Journal, WWW, RecSys, and KDD, and has been deployed to systems serving hundreds of millions of users.
University of Southampton
Doctor of Philosophy (PhD), Artificial Intelligence
January 1, 2011 – January 1, 2014
Imperial College London
MSc Artificial Intelligence
January 1, 2009 – January 1, 2010
University of Oxford
BA Computer Science
N/A – Present
Netflix
Senior Research Scientist
May 1, 2019 – Present
Los Gatos, CA
Spotify
Tech Lead
November 1, 2018 – April 1, 2019
Spotify
Senior Research Scientist
January 1, 2018 – October 1, 2018
Columbia University
Adjunct Assistant Professor
August 1, 2017 – January 1, 2018
New York, United States
Spotify
Research Scientist
December 1, 2016 – December 1, 2017
Institute for Data Sciences and Engineering at Columbia University
Postdoctoral Research Associate
July 1, 2014 – September 1, 2016
New York City Metropolitan Area
Princeton University
Postdoctoral Research Associate
February 1, 2014 – June 1, 2014
Cultural Fit Analysis
The candidate's background is heavily skewed towards advanced research science and machine learning engineering, with a strong academic foundation. While highly skilled, their profile is more aligned with a 'Research Scientist' or 'Applied Scientist' role rather than a traditional 'Data Analyst'. The projects and roles described involve developing novel algorithms and strategies, which might exceed the typical scope of a Data Analyst. The cultural fit for a pure Data Analyst role, which often emphasizes data visualization, reporting, and business intelligence, is moderate. However, for an advanced analytical role focusing on statistical modeling and experimental design, the fit would be strong.
Soft Skills & Operational Fit
The candidate's experience as a Tech Lead and Adjunct Assistant Professor suggests strong leadership, mentorship, and communication skills. Their extensive research background indicates a high degree of problem-solving ability, critical thinking, and autonomy. The focus on applied machine learning in production environments (Netflix, Spotify) demonstrates an operational fit for roles requiring practical implementation of complex models.