About N26
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.
Responsibilities
•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
Requirements
•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
Conditions
•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
