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September 3, 2026
Data Scientist (Databricks)
Mid • Remote
20,000 - 26,000 PLN/yr
Warsaw, MZ, Poland
Apply now
What you’ll do
- Design, develop and deploy machine learning models using large-scale retail and e-commerce data.
- Work with data from multiple sources and collaborate closely with Data Engineers, Analytics and Business stakeholders.
- Translate business problems into data science solutions with measurable impact.
- Work with Databricks and Spark-based data environments to prepare, analyse and model large datasets.
- Contribute to the development of production-ready ML solutions and improve existing models and approaches.
- Present findings and recommendations to both technical and non-technical stakeholders.
Your experience
- Hands-on experience with Databricks is required.
- Previous experience in e-commerce or retail is a must.
- Strong experience in Data Science / Machine Learning.
- Strong Python and SQL skills.
- Experience with PySpark / Spark and large-scale data processing.
- Good understanding of statistical modelling and ML techniques.
- Experience taking models from experimentation towards production will be a strong advantage.
- Ability to understand business needs and translate them into practical data solutions.
- Good communication skills and the ability to work effectively in a cross-functional team.
Relevant domain experience
You should have hands-on experience in at least one of the following areas:
- Customer 360 & Personalization – customer segmentation, recommendations, propensity or churn modelling.
- Demand Forecasting & Inventory – sales forecasting, inventory optimization or replenishment.
- Unified Data Platform / Analytics – working with integrated data from multiple business systems and building solutions on top of a unified data layer.
Tech stack
- Databricks
- Python
- SQL
- PySpark / Spark
- ML / AI
- Cloud
Why join?
You’ll work on a real-world, high-scale data & AI transformation in e-commerce / retail, where your models and solutions will directly support business decisions and customer-facing use cases.
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