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# PRODUCT DATA SCIENTIST

- **Company:** It Face Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Airflow, R (Programming Language), Python (Programming Language), Machine Learning, Scrum Methodology, SQL Databases, Usage Analysis, Sql Optimization, Large Language Models, Generative AI, Data Pipelines
- **Published:** September 12, 2026
- **Apply:** https://www.dice.com/job-detail/618879ef-537b-4638-b1a5-dbdbb28787d3

## About the Role

* 5+ years of experience in Data Science, Product Analytics, Applied Statistics, or a related field
 * Strong Banking, FinTech, Consumer Lending, or Financial Services experience
 * Advanced SQL skills
 * Strong Python or R programming experience
 * Strong understanding of A/B testing, experimentation, and causal inference
 * Experience developing and applying statistical and machine learning models
 * Experience with product funnels, cohorts, metrics, and product analytics
 * Experience with dbt, data pipelines, or data modeling
 * Strong communication and stakeholder management skills
 * Experience partnering closely with Product Management and Engineering teams

NICE TO HAVE

 * Home improvement lending or contractor financing experience
 * Credit, risk, or pricing modeling experience
 * Experience with Segment, mParticle, Amplitude, or similar product analytics platforms
 * Airflow or other data orchestration tools
 * Agile/Scrum experience
 * Generative AI or LLM experience

## Description

We are looking for an experienced Product Data Scientist with strong Banking, FinTech, or Consumer Lending experience to work closely with Product Management and Engineering teams., * Partner with Product Managers to translate business questions into actionable insights

 * Design, execute, and interpret A/B tests and quasi-experiments
 * Develop product metrics, funnel definitions, cohort frameworks, and analytical methodologies
 * Collaborate with Engineering teams on event tracking, instrumentation, and data quality
 * Build SQL and dbt models and support data pipelines for product analytics
 * Develop, validate, and monitor statistical and machine learning models
 * Perform propensity, conversion, segmentation, risk, and pricing analytics
 * Monitor product and experiment performance and identify business opportunities
 * Present analytical findings, insights, and recommendations to Product teams and senior stakeholders
 * Leverage AI and LLM tools for exploratory analysis and documentation with appropriate validation


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