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. ABOUT THE ROLE: WHAT'S IN THE BOX As Senior Data Scientist in the Risk and Fraud Management squad, you will be responsible for building, deploying, and maintaining machine learning models that protect HelloFresh from fraud while enabling business growth through intelligent risk assessment. You will monitor model performance for anomalies and communicate insights with stakeholders to ensure optimal impact. Your work will be crucial to prevent payment and voucher fraud at checkout, and optimize the ship & collect process for recurring customers. Hence you will have a direct impact on the company's revenue protection and customer experience goals. In this role, you will design and implement classification, regression, and uplift models which take into account various data sources, ranging from payment transactions, customer behavior, to fraud patterns and risk signals. Your models will power both real-time predictions at checkout and offline batch inference for subscription fulfillment decisions. Additionally, you will drive experiments to measure the business impact of your models and continuously improve their performance. To succeed in this role you will be curious, a fast learner, and someone who relentlessly prioritises problem statements and tailor the solution accordingly. You will be comfortable suggesting multiple solutions based on your experience, and prioritizing tasks based on effort and likely impact. As a senior member of the Data Science team, you will jump into problem solving with others as challenges arise, and collaborate with team members. WHAT YOU’LL DO: THE RECIPE * 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 WHAT YOU’LL BRING: THE INGREDIENTS  * 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 Above all, we are looking for individuals who will make HelloFresh better. We believe there are many different ways of developing skills and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you. WHAT WE OFFER: THE TOPPINGS * Global collaboration at scale: Collaborate with experienced engineers and product partners across HelloTech’s international teams, in a culture of active knowledge sharing. * Technology with real-world impact: Build and operate modern systems at global scale, supporting 6+ millions of customers and complex supply chain operations. * Technical/Product/Design leadership: Drive best practices and influence architecture/design, quality, and ways of working in an autonomous, product-led setup. * End-to-end development/delivery: Drive decisions from problem definition to production, improving systems and enabling long-term scalability. * Access to workspace at Warsaw Centre Point. The hub offers modern facilities including showers, breakout zones, outdoor space, cycle parking, and refreshments (coffee, soft drinks, and fruit). Are you the missing ingredient? If this sounds like a tasty opportunity, we’d be excited to hear from you. We aim to review your profile and respond within 5 business days. #Data