(Senior) Machine Learning Engineer – Pricing (m/f/d)

Your new team: Do you want to go beyond experimentation and bring machine learning reliably into production, where it creates real business impact? Then our Pricing team is the right place for you. You will work at the intersection of machine learning engineering and pricing, building and operating the systems that power our Dynamic Pricing platform. You will collaborate closely with Data Science, Data Infrastructure, and business stakeholders, and help shape the technical foundation of our pricing capabilities. What you can expect: Production ownership: You take end-to-end ownership of ML models from development and integration to deployment and monitoring in live environments. Reliable deployment: You operationalize machine learning models in a stable and maintainable way, and you proactively address common pitfalls when moving from experimentation to production. Snowflake enablement: You support and implement training workflows in Snowflake-based environments so model development and data platform components work smoothly across the ML lifecycle. Platform engineering: You build and further develop applications and services around our Dynamic Pricing System on Snowflake, enabling ML-driven logic in pricing workflows. Systems thinking: You keep an overview of complex system landscapes, understand dependencies and interfaces, and integrate ML components robustly into existing ecosystems. Cross-functional collaboration: You translate business and analytical requirements into technical solutions and act as a bridge between stakeholders, Data Science, and Data Infrastructure. Working model: You work remotely within Germany or hybrid at our offices in Düsseldorf or Berlin, depending on your preference and team alignment. Your profile MLOps experience: You have strong hands-on experience productionizing machine learning models and operating them reliably in productive systems. Python engineering: You write stable, production-oriented Python code and are confident with software architecture and testing, including scikit-learn pipelines. End-to-end ML lifecycle: You have experience across training, feature engineering, evaluation, monitoring, drift detection, training-serving skew, and model versioning. Data platforms: You have worked with Snowflake or comparable platforms in data-intensive and ML-related use cases. System integration: You can navigate complex landscapes across applications, services, data flows, and infrastructure, and you integrate ML components thoughtfully. Collaboration skills: You communicate clearly with technical and non-technical stakeholders and work effectively across teams and domains. Ownership mindset: You work in a structured, self-driven, and reliable way, and you take ownership from start to finish. DevOps basics: You are familiar with CI/CD pipelines and Docker. Nice to have: You bring pricing domain knowledge, for example dynamic pricing, price optimization, or pricing experimentation. Fluent English: You communicate