Data Scientist: Machine Learning and GenAI
About The Position
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. What the Work Looks Like Day to Day 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 Technical Stack 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) What Is Required 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 What Strengthens an Application 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. Working Model and Location 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.
Requirements
- 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
Nice To Haves
- 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.
Responsibilities
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
