Senior Data Scientist - Risk and Fraud Management - Growth Alliance

Work with HelloFresh in Warsaw and its HelloTech organisation, HelloFresh’s global technology backbone with more than 1000 people, building the digital products that power our end-to-end food experience. From meal kits and ready-to-eat meals to specialty offerings like pet food and premium meat & seafood, HelloTech creates the platforms that bring tailored food solutions to millions of customers every month.

Our subscription-based, direct-to-consumer model relies on technology at every step, from customer-facing apps and personalization logic to pricing, forecasting, supply chain optimization, and initiatives that help reduce food waste. While our brands operate independently to serve distinct customer needs, they are united by shared platforms, data, and operational excellence built by HelloTech.

HelloTech works in autonomous, cross-functional alliances, each owning a specific product or domain end to end. By working with our Warsaw office, you will help shape scalable, data-driven products used across our markets, working with a modern tech stack and international teams to continuously improve how people discover, order, and enjoy HelloFresh’s products, today and in the future.

• Bachelors, Masters or PhD in statistics, physics, economics, mathematics, computer science, data science or similar
• 3+ years work experience as a (senior) data scientist, preferably in e-commerce, fintech, or fraud detection
• Strong experience with classification and regression models; familiarity with uplift modeling is a plus
• Fluency in Python (Numpy, Pandas, Scikit-learn, XGBoost, LightGBM) and experience with Spark/PySpark, Databricks
• Experience with AWS services (SageMaker), workflow orchestration (Airflow/Prefect), and feature stores (Tecton is a plus)
• In-depth knowledge of handling imbalanced datasets, optimizing precision/recall trade-offs, and evaluating models in production
• Experience with real-time model inference, A/B testing, and measuring business impact of ML models
• Familiarity with software development practices (Git, Docker, CI/CD pipelines)
• Strong communication and collaboration capabilities with technical and non-technical stakeholders

• Work on fraud detection and risk assessment models from data collection to production, collaborating closely with cross-functional teams (engineering, product, finance, operations)
• Build, deploy, and maintain machine learning models for:
  • Ship & Collect: Offline batch inference for recurring payment risk assessment and subscription fulfillment decisions
  • Voucher Fraud Prevention: Real-time inference at checkout to detect and prevent voucher abuse patterns
  • Payment Fraud Prevention: Real-time inference at checkout to detect and block fraudulent payment attempts
• Monitor model performance, detect anomalies, and communicate insights and recommendations to technical and non-technical stakeholders
• Leverage classification, regression, and uplift methodologies to continuously improve fraud prevention and risk assessment solutions
• Proactively utilize Generative AI tools to accelerate development cycles, improve code quality, and drive innovative solutions to complex challenges