The opportunity
At N26, we believe our strength lies in our people and the varied perspectives they bring. We strive to build diverse teams that drive innovation and business success.
What you'll do
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.
+ years of experience as a Product Manager in a technology-driven environment.
What they're looking for
- 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.
