Product Data Scientist - Home Improvement Lending

About the role

Coforge is hiring a Product Data Scientist (remote, Mexico) embedded with Product Management to support its Home Improvement lending product. The role sits at the intersection of product analytics, experimentation and data science, turning messy data into evidence, models and instrumentation that drive underwriting funnel, borrower experience and growth decisions.

Key responsibilities

• Partner daily with Home Improvement Product Managers, turning open-ended questions into structured analyses and recommendations
• Design, run and interpret A/B and quasi-experimental tests across the borrower funnel, with attention to power, sample ratio mismatch, novelty effects and interaction risk
• Own the metrics framework: north-star and guardrail metrics, funnel and cohort definitions, and the instrumentation needed to measure them
• Work with engineering so event tracking and logging are complete, accurate and documented at the point of instrumentation
• Build and maintain data pipelines and models (SQL/dbt transformations, feature pipelines) and develop and validate statistical and ML models such as response/propensity, funnel drop-off and conversion models, with attention to fairness, explainability and regulatory context
• Use LLMs and agentic tooling to accelerate exploration, requirements gathering and documentation, while validating outputs before they inform decisions
• Communicate findings clearly, with write-ups, visualizations and recommendations tied to product or roadmap decisions, and contribute to PI planning and roadmap discussions
• Monitor product and experiment performance after launch and surface anomalies, regressions or new opportunities

Required profile

• 5+ years in a hybrid analytics/data science role (analytics consulting, product data science, applied statistics) with a track record of informing product decisions
• Bachelor's degree or higher in a quantitative field, or equivalent education and experience
• Strong grounding in statistics and experimentation, including hypothesis testing, causal inference and experiment design
• Fluent in SQL and at least one scripting/statistical language (Python or R), with data engineering fundamentals
• Able to develop, validate and communicate the trade-offs of statistical and ML models
• Uses AI tools in day-to-day work and knows when their outputs need scrutiny
• Consultant mindset: structures ambiguous problems and lands on actionable recommendations

Nice to have

• Fintech, consumer lending or home improvement/contractor financing background
• Experience with CDP platforms, event instrumentation tools (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 pipeline/orchestration tools
• Prior embedded product work in an agile/scrum environment