Roughly one-third of the world's food is never consumed - some 1.3 billion tonnes wasted each year. Our client addresses this through applied machine learning: a demand-forecasting SaaS platform that reduces fresh-food waste by up to 30% while lifting enterprise revenue.
Already trusted by Germany's largest grocery retailers and live across multiple European markets, it has raised an 8-figure Series A, reached €2M ARR, and holds contracts to reach €5M this year.
Role Overview:
You’ll join as a Senior Data Scientist, tackling one of mass retail’s toughest ML problems that affects billions in wasted food - forecasting demand and optimising inventory when products perish, stock data is imperfect and every decision has real-world consequences.
Key Responsibilities:
- Design the models that forecast the full spread of fresh-food demand, with honest uncertainty.
- Build the simulation and ordering logic that turns forecasts into orders.
- Own quality across the pipeline, from raw data to orders on shelves.
- Write clean, tested production code alongside AI coding assistants.
- Help steer technical direction and hold the bar high.
- Work with Customer Success and Engineering to lift results store by store.
Qualifications:
- You've shipped systems whose decisions carry real-world cost.
- Track record in probabilistic forecasting, stochastic inventory or operations research.
- Production Python and SQL, at ease with big, messy data.
- Hands-on with quantile regression, LGBM, conformal prediction, newsvendor models.
- Evaluate and monitor live ML; at home on GCP (BigQuery, Vertex AI), dbt, Airflow.
- You code alongside AI assistants daily.
If you want to apply serious Data Science to a problem that affects billions in wasted food – building forecasting and optimisation systems already influencing daily decisions at major European retailers – we’d love to hear from you.