Storage units and parking at half the cost of traditional storage providers. A bold, new take on storage. Backed by a16z.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
- 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
More about this role
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.
- Improve our lifetime value and unit economics models. Retrain them, re-engineer their features, and...
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