Senior Data Scientist, Growth Alliance (m/f/d)

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. ## What you’ll do: The Recipe

• Build and iterate on machine learning models powering personalization that run in production behind high-traffic, performance-critical conversion pages. - Execute decisioning models that determine the best next step in the conversion funnel for each user. - Contribute to system design by coordinating with Engineering to ensure data science solutions integrate cleanly into the overall funnel architecture. - Coordinate directly with Product and Engineering to shape how ML-driven decisions are embedded into the conversion funnels, including where models decide and how failures are handled. - Direct experimentation design for decisioning models, defining success metrics, guardrails, and how results are interpreted in high-noise, high-traffic environments. - Maintain model safety and effectiveness in production by monitoring performance, input data reliability, and latency, and iterating when issues are detected. ##