Technical Product Manager - Applied Machine Learning

We are seeking a data-driven Technical Product Manager for Applied Machine Learning to join our Intelligent Operations Platforms (IOP) segment. In this role, you will build and deploy Machine Learning solutions that empower internal teams and enable N26 to scale operations efficiently and securely.

Our ML Products team acts as the engine for data-driven decisioning. We build horizontal ML capabilities that allow crucial parts of the bank, from Financial Crime and Risk to Core Operations, to execute with speed and precision. You will work at the intersection of data science and real-world banking operations, turning complex predictive models into practical tools that improve operational efficiency and compliance.

• Execute the ML Product Roadmap: Partner closely with Senior and Lead Product Managers to deliver on the broader machine learning strategy, taking ownership of feature delivery from initial discovery through deployment.

• Bridge Data Science and Operations: Act as the day-to-day connector between Data Scientists, ML Engineers, and business stakeholders. Translate operational pain points into clear technical specifications, user stories, and acceptance criteria.

• Drive Operational and FinCrime Automation: Deliver predictive models that solve concrete internal bottlenecks, focusing on automating back-office tasks, accelerating document verification, and improving fraud detection accuracy.

• Monitor Model Health and Business KPIs: Define, track, and analyze performance metrics for live models. Monitor production accuracy, latency, and false positive rates to identify opportunities for continuous retraining and optimization.

• Drive Squad Backlog Execution: Partner with engineering leads to groom product backlogs, write detailed user stories, and participate in daily sprint rituals, ensuring a steady, reliable pace of delivery while upholding strict security standards.

• 4+ years of experience as a Product Manager in a technology-driven environment.

• Hands-on experience with, or a very strong interest in, data products, analytics, or Machine Learning workflows.

• Data Fluency & SQL: Highly comfortable querying and analyzing datasets independently. Strong ability to use SQL and data visualization platforms to evaluate model impact and validate hypotheses.

• Core ML Fundamentals: Solid understanding of basic machine learning concepts, including classification, regression, decision trees, and the standard model lifecycle from data collection to deployment.

• Model Evaluation Understanding: Ability to translate technical model evaluation metrics such as precision, recall, and F1 score into practical operational outcomes like saved handling hours or reduced fraud losses.

• Technical Communication: Skill in translating complex business rules into precise requirements for engineers, while clearly explaining model outputs and limitations to operational teams.

• Execution Bias: Focus on delivering pragmatic, incremental value quickly rather than waiting for perfect data.

• Analytical Problem Solver: Curious about complex backend processes, with a natural drive to uncover the