Job Description

W2 Role, locals only & Banking domain experience

Role : Product Data Scientist
Location: SFO, CA Hybrid

Hybrid: 3 days in SFO. 2 days WFH

Total Experience level:10+

Product Data Scientist, Home Improvement

About The Role:

This role sits at the intersection of product analytics, experimentation, and data science - embedded directly with Product Management to help shape and grow our Home Improvement lending product. You'll be the analytical backbone for a product team making high-stakes decisions about underwriting funnels, borrower experience, and growth, translating messy data into clear evidence and, where it counts, into models and instrumentation that ship. It's a high-impact role for someone who is equally comfortable running a rigorous A/B test, writing a dbt model, and explaining a lift curve to a VP.

What You'll Do

• Partner day-to-day with Home Improvement Product Managers as their embedded data science and analytics resource - turning open-ended product questions into structured analyses and clear recommendations.
• Design, run, and interpret experiments (A/B and quasi-experimental) across the borrower funnel - from offer presentment through origination - with rigor around power, sample ratio mismatch, novelty effects, and interaction risk across concurrent tests.
• Define and own the metrics framework for the Home Improvement product line: north-star and guardrail metrics, funnel and cohort definitions, and the instrumentation needed to measure them reliably.
• Work with engineering to ensure event tracking and logging are complete, accurate, and well-documented at the point of instrumentation, not discovered as gaps after the fact.
• Build and maintain data pipelines and models (e.g., SQL/dbt transformations, feature pipelines) well enough to be self-sufficient for most analyses and to collaborate credibly with data engineering on the rest.
• Develop and validate statistical and ML models supporting product decisions - response/propensity models, funnel drop-off and conversion models, segmentation, and early-stage risk or pricing signals in partnership with credit strategy - with attention to fairness, explainability, and regulatory context appropriate to a lending business.
• Bring AI fluency to the work: use LLM- and agentic-tooling to accelerate exploratory analysis, requirements gathering, and documentation, while knowing where automated outputs need human judgment and validation before they inform a decision.
• Communicate findings in a way that drives action - clear write-ups, well-chosen visualizations, and recommendations tied to specific product or roadmap decisions, not just descriptive dashboards.
• Contribute to PI planning and roadmap discussions by sizing opportunities, flagging measurement risk in proposed initiatives, and helping the team commit to work that can actually be evaluated.
• Continuously monitor product and experiment performance post-launch, and proactively surface anomalies, regressions, or new opportunities rather than waiting to be asked

.

About You

• 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.

Time Zone Requirements

Flexible, with core overlap expected with Pacific Time hours

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