Jared Donohue

I'm a product data scientist focused on improving products through better measurement, customer insights, and testing new product hypotheses. I spent 5+ years at Amazon running A/B tests and shaping product decisions, and more recently I've partnered directly with startup founders to instrument product analytics, influence strategy, and ship key user-facing features. I hold an MS in Data Science from Columbia, where I'm currently a Staff Research Associate working with professors at Columbia Business School on applied causal inference and experimentation projects.

Alexa+

Launched innovative audiovisual screen responses for Alexa+, the next-generation of Alexa powered by generative AI. Read more here.

Echo Show

Increased device interaction and customer satisfaction with Alexa's smart display devices. Read more here.

AlexaAnswers.com

Led 10x growth in monthly active users through product experiments and marketing expansion. Read more here.

Mayah Design

Built AI image analysis & product analytics to boost customer satisfaction for Columbia Business School startup: Mayah Design.

HealthEvidence.org (personal project)

Surfaces ClinicalTrials.gov studies and grades them by evidence quality, making it easy to distinguish randomized intervention trials from weaker observational designs at a glance.

Eatopia (personal project)

A nutrition app prototype that uses behavioral science principles to nudge users toward healthier eating.

Guest Lecture: Using LLMs for Science

Guest lecture at CUNY Graduate Center for class of 15 PhD and master's students on practical methods and workflows for using large language models in scientific research: PDF

Scientific Data Creation & Validation using LLMs

Published "Structured dataset of reported cloud seeding activities in the United States (2000–2025) using an LLM". Built pdf-to-text pipeline with OpenAI integration to extract metadata from 1,000+ scanned NOAA reports, addressing a gap in structured cloud seeding data: Scientific Data (Nature Portfolio)

Cloud Seeding Difference-in-Differences Analysis

Causal Analysis of Cloud Seeding with Difference-in-Differences

Estimated the causal effect of cloud seeding on precipitation using a within-site difference-in-differences design across U.S. seeding sites (2000–2025). Combined the structured NOAA cloud seeding dataset with PRISM and ERA5 precipitation data to compare seeded vs. unseeded months within target and control areas, with results delivered through an interactive Streamlit app: Streamlit App | GitHub

Behavioral Economics Experiment

Experiment design test for level-k strategic route choices in navigation apps (e.g. Google Maps): PDF

M.S. Data Science, Columbia University

Data Science Institute Scholar
Columbia Build Lab Intern
External Guest Lecture (CUNY Graduate Center): Using LLMs for Science (PDF)

B.S. Computer Science, George Mason University

Computer Science Teaching Assistant and Peer Mentor

Researcher and Product Data Scientist

About Me

Product