Data Scientist - AI & Experimentation (m/f/d)
Indexed description
About The Role
We're looking for a Data Scientist who treats AI as a working tool, not a buzzword. You'll sit at the intersection of statistics, machine learning, and product: building predictive models, improving our LLM- and RAG-based systems, and running experiments that directly shape how our platform matches supply and demand. Your work won't end at a slide deck. You'll define the metrics, ship the analysis, and follow through until the impact shows up in the numbers.
Tasks
- Build, validate, and ship statistical and predictive models that directly inform pricing, matching, and growth decisions
- Develop and improve LLM-powered features, from retrieval-augmented generation (RAG) pipelines to applications of new AI technologies that open up product innovation
- Own the reliability of our AI features: design prompt and evaluation workflows, measure output quality, and catch regressions before users do
- Turn open questions into testable hypotheses and design experiments (e.g., A/B tests) that give clear, decision-ready answers
- Dig into funnels and user journeys to find drop-offs and friction points, and quantify where supply and demand can be better matched
- Team up with performance marketing to sharpen targeting, attribution, and campaign efficiency with data
- Define the KPIs that matter, build the dashboards and monitoring behind them (AWS QuickSight), and make business impact visible and measurable
- Keep your work transparent and traceable: document, prioritize, and communicate progress in Jira across product, engineering, and marketing
- Present findings to stakeholders as concrete recommendations, then stay involved until they're implemented
- You love to work with data: explore it, model it, improve its quality.
- Deep grounding in statistics: you know which method fits which problem and can defend your assumptions, not just run the library defaults
- Fluent in Python (pandas, scikit-learn, NumPy) and SQL, with a track record of applying them to real business problems rather than toy datasets
- Hands-on experience taking ML and modern AI techniques from idea to a working solution that someone actually uses
- Practical experience with LLMs and RAG systems in production or near-production settings, including prompting, retrieval quality, and output evaluation
- Solid command of A/B testing: sample sizing, significance, common pitfalls, and knowing when an experiment is the wrong tool
- Working knowledge of performance marketing concepts such as CAC, ROAS, and attribution logic
- Project experience in at least one of: anomaly detection, trend analysis, marketing mix modeling, or multi-touch attribution
- Background in e-commerce, marketplaces, or other platform-based businesses, ideally with exposure to supply and demand dynamics
- Bonus: degree in mathematics, statistics, physics, computer science, or a related quantitative field
- Flat hierarchies with short decision-making paths (start-up mentality) and an open corporate culture with helpful & communicative colleagues
- Regular feedback conversations
- A pleasant workplace (open-plan office centrally located in Berlin-Mitte) with home office option
- A very nice and cooperative working atmosphere
- Free drinks
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