Job Title: Product Data Scientist Key Skills: Python or R, SQL, Data Experience: 6+ years Location Hermosillo or Mexico City Mode: Onsite 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 than waiting to be asked Required Qualifications - 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 Posted On: September 8, 2026 At Coforge, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

📌 Product Data Scientist (Santiago de Querétaro)
🏢 Encora
📍 Santiago de Querétaro