Senior Data Scientist, Demand Forecasting, Food Systems

HelloFresh is a global company with a HelloTech organisation employing over 1000 people, developing digital products for end-to-end food experiences including meal kits, ready-to-eat meals, pet food, and premium meat & seafood. HelloTech builds shared platforms used across multiple brands and markets, focusing on subscription-based, direct-to-consumer models that involve pricing, forecasting, supply chain optimization, and food waste reduction. The Warsaw office collaborates internationally on scalable, data-driven products using modern tech stacks.

• Bachelor's, Master's, or PhD in econometrics, statistics, computer science, physics, economics, mathematics, data science or similar.
• 3+ years of experience as a (senior) data scientist focused on demand forecasting and developing time series and causal models.
• Proficiency in Python and its scientific libraries including NumPy, pandas, scikit-learn, and Matplotlib.
• Experience with Databricks and PySpark is a plus.
• Strong knowledge of statistical modeling, handling uncertainties, and applying regressors and classifiers.
• Ability to retrieve, manipulate, and analyze large heterogeneous datasets using SQL.
• Experience working in cloud environments like AWS, GCP, or Azure for data science and ML workloads.
• Familiarity with version control and collaboration workflows using Git.
• Understanding or hands-on experience with GenAI-supervised development workflows.
• Strong communication and collaboration skills to work with international teams and stakeholders.
• Domain knowledge in supply chain is a big plus.
• Enthusiasm for learning, critical thinking, creative problem solving, and teamwork.

• Take technical ownership of demand forecasting time series and causal models from data collection to operationalization.
• Collaborate with cross-functional teams including product, demand planning, and engineering to translate business objectives into data-driven strategies.
• Propose new approaches and evolve forecasting models aligned with company strategies.
• Experiment and explore improvements to forecasting methodologies.
• Work with technologies like AWS (EMR, Glue, S3), Kafka, PySpark, Airflow, and Databricks throughout the ML lifecycle.
• Mentor and support data scientists and colleagues to foster knowledge sharing.
• Communicate and collaborate with stakeholders including demand planners, ML engineers, product managers, and business stakeholders.