Product Data Scientist

Job Title: Product Data Scientist Key Skills: Python or R, SQL, Data Experience: 6+ years Location: Mexico Mode: Remote We at Coforge are hiring Position (17739-PE-MX-TM2-6) with the following skill set. 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. Responsibilities 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…