About Me
I’m a product data scientist and researcher with experience spanning large-scale experimentation at Amazon, startup analytics and product development, and applied causal inference research at Columbia Business School. My work focuses on experimentation, causal inference, and using data to better understand product and human behavior.
Selected Work
LLM-Based Cloud Seeding Dataset and Causal Analysis
Published "Structured dataset of reported cloud seeding activities in the United States (2000–2025) using an LLM" in Scientific Data. Built a PDF extraction pipeline with OpenAI integration to process 1,000+ scanned NOAA cloud seeding reports into a structured dataset, helping address the gap in cloud seeding data. Conducted a within-site difference-in-differences analysis to estimate the causal effect of cloud seeding on precipitation and developed an interactive Streamlit dashboard to explore the results.
Alexa+ Smart Display Experiences
Improved engagement and customer experience across Alexa smart display devices. Helped launch multimodal screen experiences for Alexa+, the next-generation of Alexa powered by generative AI. Read more here.
Product Science, LLC
Founder of Product Science Consulting, helping early-stage startups get from insights through measurable impact. Representative work includes a deep product and engineering partnership with Mayah Design, supporting the founder with product strategy, product development, and analytics end to end.
Education
M.S. Data Science, Columbia University
- Courses in: Design & Analysis of Online Experiments, Causal Inference, Behavioral Economics
- Data Science Institute Scholar
- Columbia Build Lab Intern
- Guest Lecture @ CUNY Graduate Center: Using LLMs for Science (PDF)
- Behavioral Economics research proposal (experiment): Strategic Route Choice in Navigation Apps: Testing for Level-K Thinking (PDF)
