Remote: Product Data Scientist with Fintech, Consumer Lending, Or Home Improvement/Contractor Financing.

Remote: Product Data Scientist with Fintech, Consumer Lending, Or Home Improvement/Contractor Financing.

Work Location: Remote / Flexible —Occasional travel to offices may be required as needed.

Contract: 12 Months

Video Interview

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.

Must 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

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:

• 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