Data Science Internship (Current PhD) - Summer 2027

We're excited to add a Data Scientist Intern to our Data & Analytics team. You'll work on the machine learning models we already run in production, including lifetime value, unit economics, and forecasting, making them more accurate and showing with evidence that they've improved. You'll also help Product, Marketing, and Sales divisions design A/B tests and interpret the results. Your work feeds directly into decisions across a marketplace that operates in nearly every U.S. city. This is a great fit for a PhD student who wants to apply their research skills to live business problems. You'll report to our Data & Analytics manager, with regular code review and hands-on mentorship.

Our stack: Python, dbt, and Dagster on Redshift and Athena, with a Cube semantic layer and Superset for BI.

The Problems You'll Solve

• Improve our lifetime value and unit economics models. Retrain them, re-engineer their features, and validate their predictions against realized outcomes.
• Audit inherited models: find the leakage, the stale hardcoded assumption, the segment where performance quietly falls apart, and the feature that's doing less work than everyone believes.
• Build forecasts our operators actually plan against: demand and supply by market, revenue, and the levers that move them.
• Partner with Product, Marketing, and Sales to design tests before they launch. Catching an underpowered test in the design review is worth more than any analysis you can do afterward.
• Analyze results and make a call and be candid about what each design can and can't identify
• Become a subject matter expert on Neighbor's product, users, and marketing life cycle. The modeling is the easy part; knowing which features mean something is the hard part.

Qualifications

• PhD-level candidate currently enrolled  in a quantitative field (Computer Science, Statistics, Math, Physics, Data Science, etc.) completing your PhD by May of 2028; transcript required
• Demonstrated research, coursework, or project experience applying statistics and machine learning to real-world or complex datasets
• Strong SQL and fluent Python for modeling are required; experience with dbt or semantic layers like Cube is a big plus, but we're happy to train you on our specific transformation stack
• Academic or project-based experience with A/B testing design or statistical hypothesis testing
• Real grounding in inference, not just fitting: you can explain what your confidence interval means, why the p-value moved when you added a second metric, how leakage sneaks into a validation split, and when you don't have the data to answer the question
• Strong problem-solving mindset and eagerness to diagnose and improve existing code and statistical models
• Intellectual stubbornness in a productive direction: you keep pulling on a thread when the numbers don't reconcile, and you don't ship an explanation you don't believe
• Clear communication with non-technical stakeholders: you can explain a result and its uncertainty without either overclaiming or hiding behind jargon