Data Scientist Machine Learning and GenAI

This is an ownership driven data science position within a scaled globally distributed hub focused on bringing algorithms to production. The work spans traditional machine learning deep learning GenAI optimization and statistical modeling. Methods are chosen based on the problem not the trend. The scope covers high impact business domains including retail media digital commerce supply chain R&D and productivity.

This is not a research only role. The expectation is to understand the business problem deeply build the right model and see it through to reliable production deployment.

• Take ownership of a defined business domain and its algorithmic needs from problem framing through to deployed solution

• Partner with product business and AI engineering teams to automate and integrate models into live applications

• Analyze large scale datasets (think: processing billions of behavioral signals daily) and translate findings into actionable recommendations

• Define and evolve the algorithmic roadmap for your area of ownership

• Apply machine learning statistical optimization and GenAI techniques to real business problems

• Write production grade code following engineering best practices

• Build resilient maintainable algorithmic pipelines that hold up over time

• Cloud: Microsoft Azure Google Cloud Platform Kubernetes

• Languages: Python Spark (preferred); SQL for analytical work

• Big data ecosystem: Databricks BigQuery Spark

• Dev tools: GitHub Jira Confluence (Agile DevOps environment)

• BI tools: PowerBI or Tableau (basic familiarity useful)

• Masters degree in a quantitative field (Statistics Operations Research Computer Science Applied Mathematics Systems Engineering Economics) OR a Bachelors or Engineering degree with solid consecutive data science experience

• At least 2 years of experience delivering production grade data science or algorithmically enabled applications

• Strong Python skills with hands on experience in machine learning statistical modeling and optimization

• Solid SQL and analytical skills

• Demonstrated ability to lead problem solving and prioritize across competing demands

• Comfortable working across cross functional teams in a fast moving environment

Experience with the full lifecycle of an algorithmic product: not just model building but deployment monitoring and iteration. Familiarity with big data tooling (Databricks BigQuery Spark) and exposure to GenAI or optimization methods are genuine advantages not box ticking requirements.

This role is based in Warsaw Poland on a hybrid working arrangement. Regular on site presence in Warsaw is expected; full remote is not available for this position.