Product Data Scientist

5+ years of experience in a hybrid analytics/data science role (e.g., analytics consulting, product data science, applied statistics) with a track record of directly informing product decisions; bachelor’s degree or higher in a quantitative field, or equivalent combination of education and experience

You have strong grounding in statistics and experimentation — hypothesis testing, causal inference, experiment design, and you can explain the difference between a significant result and a meaningful one

You’re fluent in SQL and at least one scripting/statistical language (Python or R), and you’re comfortable enough with data engineering fundamentals (pipelines, transformations, data modeling) to build what you need and partner effectively with engineers on the rest

You can develop, validate, and communicate the tradeoffs of statistical and machine learning models, and you know when a simpler model or a well-designed experiment beats a complex one

You use AI tools in your day-to-day work — for exploratory analysis, documentation, and accelerating routine analytics — and you know when their outputs need scrutiny before they touch a product decision

You think like a consultant: you get to the real question behind the question, structure ambiguous problems, and land on recommendations stakeholders can act on

You have good judgment about rigor versus speed, and you don’t cut corners on measurement integrity just to hit a deadline

You’re a clear communicator who can flex between a technical conversation with engineering and a decision-focused conversation with product and business stakeholders

You’re curious about how data, experimentation, and AI can change what’s possible in consumer lending products, and you’re always looking for a better way to answer the question

Nice to Have

Background in fintech, consumer lending, or home improvement/contractor financing

Experience with CDP platforms, event instrumentation tooling (e.g., Segment, mParticle, Amplitude), or experimentation platforms

Hands-on experience with credit or risk modeling, pricing strategy, or marketing decisioning

Experience with dbt, Airflow, or similar data pipeline/orchestration tools

Prior experience embedded directly with product teams in an agile/scrum environment