About Me
Product data scientist with eight years at Amazon Alexa, the last four in product, and an M.S. in data science from Columbia, focused on causal inference. At Amazon, I grew Alexa Answers website monthly active users 8x and Echo Show screen engagement 5x through A/B testing. I also started an experiment review program for 50+ product managers and engineers. Now I run online experiments and causal studies at Columbia Business School on how people and firms adopt AI. I’m looking for product manager or product data scientist roles at mission-driven consumer companies, where experiments answer key questions about user behavior.
Selected Work
Amazon AlexaAnswers.com
Grew monthly active users 8x, from 5,000 to 40,000 in 12 months, through 15 A/B tests on the conversion funnel and new acquisition channels. Ran research that identified the top churn drivers and prioritized the fixes that shipped. Read more here.
Amazon Alexa+ Smart Display Experiences
Grew screen engagement 5x in 12 months through funnel analysis and 10 A/B tests on design and personalization. Defined a visual relevance metric for Alexa+ that lifted visual response quality from 60% to 94%. Started an experiment review program that set design standards for 50+ product managers and engineers. Read more about Alexa+.
Columbia Business School
I run experiments and causal studies with faculty in the Decision, Risk & Operations division on how people and firms adopt AI. Current work includes a difference-in-differences analysis of how 2,000+ firms are changing their experimentation practices with AI, an online experiment on how developers decide whether to accept AI-written code, and a systematic review of AI-enabled health and education randomized controlled trials.
Product Science, LLC
Founded Product Science Consulting to help early-stage startups get from insights through measurable impact. Partnered with the founder of an interior design technology company to improve product strategy, setup analytics infrastructure end to end, and deploy customer-facing AI features. Running DesignMyExperiment.com to provide free help with experiment design, including curated libraries of real product A/B tests and methodology reviews.
Research Publication: Extracted Environmental Dataset and Causal Analysis
First author of Structured dataset of reported cloud seeding activities in the United States (2000–2025) using an LLM, published in Scientific Data (Nature Portfolio). Built a PDF extraction pipeline with OpenAI integration to process 800+ scanned NOAA cloud seeding reports into a structured dataset, helping address the gap in cloud seeding data. Now conducting a within-site difference-in-differences analysis to estimate the causal effect of historical cloud seeding operations on precipitation: Difference-in-Differences Explorer. Presented the methods behind this work and broader topic of "Using LLMs for Science" in a guest lecture at the CUNY Graduate Center for 15 PhD and master's students: Guest Lecture: Using LLMs for Science (PDF).
Education
