Commercial Data Scientist: Churn, Attribution & Revenue Growth

Planet; Warszawa; Híbrido; PLN 180.000 - 280.000; Jornada completa

Descripción de la vacante

Planet is seeking a Data Scientist to join our AI team and lead commercial data science work. You will partner with Sales, Marketing, Customer Success, and Finance to turn customer and revenue data into models and insights that move the top line — reducing churn, surfacing cross-sell opportunities, and giving the business a clear view of revenue drivers.

This is a hands-on, high-impact role that balances rigorous modelling with pragmatic delivery and end-to-end ownership of projects.

Formación

• 5+ years in applied data science with commercial focus (churn, LTV, attribution, pricing).
• Strong SQL and Python; able to ship production-grade code, not just notebooks.
• Hands-on with Snowflake or comparable data warehouses; experience deploying models on AWS.

Responsabilidades

• Churn modelling — build, deploy, and iterate on customer churn prediction models.
• Cross-sell & upsell — develop propensity models and next-best-action recommendations.
• Revenue attribution & tracking — design attribution frameworks across channels and product touchpoints.
• Experimentation — design and analyse A/B tests and quasi-experiments for commercial initiatives.
• Productionisation — take models from notebook to production with full lifecycle management.
• Stakeholder partnership — translate business questions into analytical problems and communicate findings clearly.

Conocimientos

SQL

Python

Snowflake

AWS

Statistics

Experimentation

CAUSAL INFERENCE

Descripción del empleo

Planet is seeking a Data Scientist to join our AI team and lead commercial data science work. You will partner with Sales, Marketing, Customer Success, and Finance to turn customer and revenue data into models and insights that move the top line — reducing churn, surfacing cross-sell opportunities, and giving the business a clear view of revenue drivers.

This is a hands-on, high-impact role that balances rigorous modelling with pragmatic delivery and end-to-end ownership of projects.