Data Science Internship (Current PhD) - Summer 2027
A PhD-level data science intern on Neighbor's Data & Analytics team will improve production machine learning models (lifetime value, unit economics, forecasting) and help Product, Marketing, and Sales design and interpret A/B tests. Core stack: Python, SQL, dbt, Dagster, Redshift/Athena, Cube, and Superset.
- Compensation
- Not disclosed
- Location
- Lehi, UT
- Work setup
- Onsite
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Currently enrolled PhD candidate in a quantitative field (Computer Science, Statistics, Math, Physics, Data Science, etc.), completing PhD by May 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
- Academic or project-based experience with A/B testing design or statistical hypothesis testing
- Real grounding in statistical inference: confidence intervals, p-values, data leakage in validation splits, and knowing when the data can't answer the question
- Strong problem-solving mindset and eagerness to diagnose and improve existing code and statistical models
- Intellectual persistence when numbers don't reconcile; won't ship explanations they don't believe
- Clear communication of results and uncertainty with non-technical stakeholders
Nice to have
- Experience with dbt or semantic layers like Cube
Full description
At Neighbor, we’re building the largest hyperlocal marketplace the world has ever seen. We’ve raised over $75 million from top-tier investors such as Andreessen Horowitz and the CEOs of DoorDash, StockX, and Uber. Our marketplace is already flourishing in all 50 states and we’re just getting started!
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