product manager for a Machine Learning platform

N26 is a digital bank that offers an elegant digital banking experience without physical branches, paperwork, or hidden fees. Its platform helps people manage their banking and savings digitally.

• Define and execute the multi-year roadmap for the central Machine Learning platform, aligning it with engineering architecture and executive business goals
• Oversee predictive ML capabilities for real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning
• Own platform tooling and standards across the ML lifecycle, including feature stores, real-time feature extraction pipelines, model registries, automated retraining triggers, and CI/CD for ML
• Define offline and online model evaluation frameworks, shadow deployment mechanics, champion and challenger testing, and real-time monitoring for model drift, inference latency, and data quality degradation
• Partner with Principal Data Scientists, ML Engineers, Security, and Legal stakeholders to ensure models meet European banking governance, explainability, and auditability standards
• Establish product best practices, PRD standards, and self-service ML integration blueprints, and mentor Product Managers

• 7+ Years of Product Management experience in a technology-driven environment, including at least 4 years focused on building, scaling, and managing core Machine Learning platform capabilities or data infrastructure
• Proven track record of shipping ML products in high-concurrency, low-latency environments such as FinTech, payment processors, or high-scale consumer technology platforms
• Experience leading multi-squad initiatives, shaping platform strategy across cross-functional engineering triads, and presenting to C-level executives and regulatory bodies
• Hands-on understanding of predictive ML methods, including classification, regression, clustering, anomaly detection, and ranking
• Architectural mastery of modern MLOps pipelines, including Kafka event streams, feature stores, vector databases, and automated retraining
• Proficiency in defining model performance metrics such as precision, recall, ROC-AUC, and F1-score, and translating them into business ROI
• Ability to evaluate trade-offs between real-time inference and batch scoring, feature compute complexity and latency SLAs, and model accuracy and explainability
• Deep understanding of regulations for automated financial decisioning, model transparency, GDPR data privacy, and audit trails in regulated environments
• Ability to turn domain-specific requirements into reusable horizontal ML services for multiple engineering squads
• Ability to navigate ambiguity, make high-stakes technical trade-off decisions, and prioritize foundational infrastructure alongside feature delivery
• High standards for system resilience, monitoring, and fail-safe mechanisms; understanding that core banking engines demand 99.99% reliability

• Competitive personal development budget, work from home budget, fitness and wellness discounts, and public transportation subsidies
• Premium N26 bank account subscription for the employee, plus subscriptions for friends and family
• One additional annual leave day for each year of service
• Relocation package with visa support for those who need it