About The Role

ABOUT THE ROLE

You'll join a small, senior Data Science team tackling one of the hardest forecasting problems in retail: fresh produce ordering. Every decision is a daily tradeoff between waste and availability, with immediate, visible impact in live stores — this is production ML with real operational consequences.

WHAT YOU'LL DO

Develop and improve distributional forecasting models, working across architecture, calibration, and coverage.
Monitor forecast quality metrics consistently and act swiftly when performance degrades.
Contribute meaningfully to inventory simulation and ordering policy frameworks, driving improvements through to production.
Shape technical direction in your focus area, contributing to methodology decisions and upholding high standards for code quality and production readiness.
Own end-to-end pipeline quality, from input data integrity through forecast output to live order recommendation performance.
Conduct ongoing monitoring and regular backtesting evaluations to assess model impact before issues reach stores.
Ship production-quality, well-tested, readable code with thorough review.
Leverage agentic AI tooling to work efficiently without compromising on craft.
Collaborate with Customer Success and Engineering to translate store-level findings into product improvements.

WHAT WE'RE LOOKING FOR

5+ years building systems that make decisions under uncertainty with real operational consequences — not research prototypes.
MSc or PhD in a quantitative field (Statistics, Mathematics, Physics, Operations Research, Computer Science, or similar).
Background in probabilistic forecasting, stochastic inventory simulation, or operations research, with solid working knowledge across all three.
Deep familiarity with distributional and probabilistic forecasting methods: quantile regression, LGBM with distributional output, GAMLSS-type models, conformal prediction.
Solid grounding in stochastic inventory theory, newsvendor models, and service level optimisation.
Production-grade Python and SQL on large, messy, real-world datasets.
Experience with ML model evaluation, monitoring, and backtesting in production environments.
Comfortable with GCP (BigQuery, Cloud Run, Vertex AI), dbt, and workflow orchestration (Cloud Composer, Airflow).
Strong software engineering practices: Git, containerisation, CI/CD.
Daily fluency with agentic AI coding tools (e.g. Claude Code, Cursor, or similar).
Fluent in English; German or French is a strong plus.
Domain knowledge in supply chain optimisation, demand planning, or perishables/grocery retail is a valuable plus.
Prior startup or scale-up experience is a plus.