Senior Product Data Scientist — Growth & Experiments

encora10; Mexico; On-site; PHP 2,950,000 - 4,425,000; Full time

Job summary

Coforge is seeking a Product Data Scientist to join our Home Improvement lending product team. You will be embedded with Product Management, turning product questions into structured analyses and actionable recommendations.

The role involves designing experiments, defining metrics, and building data pipelines to support underwriting, borrower experience, and growth decisions. A strong analytics background and SQL/Python or R are essential.

Qualifications

• 5+ years in hybrid analytics/data science roles directly informing product decisions.
• Strong grounding in statistics, experimentation, and causal inference.
• Fluent in SQL and Python or R; familiar with data engineering fundamentals.

Responsibilities

• Partner day-to-day with Home Improvement Product Managers as embedded data science resource.
• Design, run, and interpret experiments (A/B and quasi-experimental) across the borrower funnel.
• Define and own metrics framework for the Home Improvement product line and instrumentation.
• Collaborate with engineering to ensure event tracking and data quality.
• Build and maintain data pipelines and models for analyses and collaboration with data engineering.
• Develop and validate statistical and ML models to support product decisions with fairness and explainability.
• Leverage AI tools to accelerate analyses while ensuring human validation for decisions.
• Communicate findings with clear write-ups and visuals tied to product decisions.
• Contribute to PI planning by sizing opportunities and assessing measurement risks.
• Monitor product and experiment performance post-launch and surface anomalies.

Skills

Python

SQL

Data analysis

Education

Bachelor's degree or higher in a quantitative field

Job description

Coforge is seeking a Product Data Scientist to join our Home Improvement lending product team. You will be embedded with Product Management, turning product questions into structured analyses and actionable recommendations.

The role involves designing experiments, defining metrics, and building data pipelines to support underwriting, borrower experience, and growth decisions. A strong analytics background and SQL/Python or R are essential.