Technical Lead — Data Science (f/m/x)

We’re looking for a technical lead who can do two hard things at once: keep a team of data scientists shipping work that holds up to scrutiny, and stay close enough to the code to know when it doesn’t. You’ll own the technical direction of data science projects end-to-end — from framing the problem with the client to signing off on the modeling approach, reviewing the work, and making sure what gets delivered actually runs.

This is a hands-on role. You’ll still write Python, still challenge model choices, still dig into why the metrics look too good. But your biggest impact will come through others — setting standards, mentoring the team, and making the technical calls that keep projects on track across platforms like Microsoft Fabric, Palantir Foundry, Databricks, and the major clouds.

What we offer:

AI Grant — Stop talking about AI and start building it. Our AI Grant gives you dedicated budget and resources to turn your wildest AI idea into a working project, backed by two paid weeks to focus on nothing else.

AI Center of Excellence — Work alongside specialists in agentic AI, sovereign AI, generative and discriminative AI. This isn’t a siloed team — it’s the people you’ll learn from and build with daily.

Your tools, your choice — Full access to AI-powered development tools including Claude, Cursor, and GitHub Copilot. Pick what works best for you.

Real project variety — From generative AI for legal document compliance, through agentic systems in manufacturing environments, to enterprise-scale AI platforms, computer vision, and autonomous driving. You won’t get bored.

Conference and speaking support — Want to attend conferences? We’ll back you. Want to speak at them? Even better — we’ll support you with dedicated preparation time and bonuses.

Your tasks

• Own the technical direction of data science engagements: solution design, modeling approach, validation strategy, and delivery quality

• Lead and mentor a team of data scientists — code reviews, methodology reviews, career development, and raising the bar on what "done" means

• Stay hands-on: prototype approaches, unblock the team on hard modeling problems, and contribute production-grade Python where it matters most

• Translate client business problems into scoped, estimable data science workstreams, and push back when the problem doesn’t need a model

• Set and enforce engineering standards for the team: reproducibility, experiment tracking, testing, and documentation

• Guide platform decisions and delivery across cloud/data ecosystems — Microsoft Fabric, Palantir Foundry, Databricks, Snowflake, and native Azure/AWS/GCP services

• Work with AI Architects, data engineers, and client stakeholders to align technical decisions with business outcomes and timelines

• Represent the team in front of clients: present results, defend methodology, and manage technical expectations

Requirements

• At least 6 years in data science with a track record of models shipped to production, including a minimum of 2 years leading or mentoring other data scientists

• Deep, hands-on Python skills across the standard data stack (pandas, NumPy, scikit-learn, XGBoost) — you can still review and write the hard parts yourself

• Strong statistical and ML foundations: experimental design, validation methodology, and the judgment to spot results that won’t survive contact with reality

• Familiarity with at least one cloud/data platform and its data science components — Azure (Microsoft Fabric, Azure Machine Learning), AWS (SageMaker), GCP (Vertex AI, BigQuery ML), Snowflake (Snowpark ML, Cortex), Databricks (MLflow, Mosaic AI), or Palantir (Foundry Code Workspaces, Foundry ML, AIP)

• Experience leading delivery in a client-facing or consulting context: scoping, estimation, and managing technical risk

• Strong communication skills — able to defend a methodology to a technical audience and explain its business impact to a non-technical one

• Fluent English (both written and spoken)

• Fluent Polish required

• Residing in Poland required

Nice-to-have requirements

• Production experience with Microsoft Fabric or Palantir Foundry, or relevant platform certifications

• Experience with MLOps at team scale: CI/CD for ML, model registries, monitoring, and governance

• Exposure to GenAI/LLM projects and judgment about where they do — and don’t — belong in a data science solution

• Public technical presence: conference talks, publications, or open-source contributions