Description
We are seeking an experienced Lead Technical Product Manager for our Machine Learning Platform within the Intelligent Operations Platforms segment. In this role, you will define the long term vision, platform architecture, and execution strategy for core Machine Learning infrastructure across N26. Your work will enable squads across the bank to automate risk assessments, prevent financial crime, and scale operational efficiency.
Responsibilities
•Define and execute the multi-year roadmap for the central Machine Learning platform, ensuring alignment with broader engineering architecture and executive business goals.
•Oversee complex predictive ML capabilities supporting high-throughput banking systems, focusing on real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning.
•Own the platform tooling and standards across the full ML lifecycle.
•Drive strategic decisions around feature stores, real-time feature extraction pipelines, model registries, automated retraining triggers, and CI/CD for ML.
•Define robust offline and online model evaluation frameworks, shadow deployment mechanics such as champion and challenger testing, and real-time monitoring for model drift, inference latency, and data quality degradation.
•Partner closely with Principal Data Scientists, ML Engineers, Security, and Legal stakeholders to ensure models meet strict European banking governance, explainability, and auditability standards.
•Serve as a functional leader within the product organization.
•Establish best practices, PRD standards, and self-service ML integration blueprints while mentoring Product Managers in the team.
Requirements
•7+ years of Product Management experience in a technology-driven environment, with at least 4 years deeply focused on building, scaling, and managing core Machine Learning platform capabilities or data infrastructure.
•Proven track record of shipping ML products within high concurrency and low latency environments such as FinTech, payment processors, or high-scale consumer tech platforms.
•Demonstrated 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 methodologies including classification, regression, clustering, anomaly detection, and ranking, paired with architectural mastery of modern MLOps pipelines such as Kafka event streams, feature stores, vector databases, and automated retraining.
•Highly proficient in defining model performance metrics like precision, recall, ROC-AUC, and F1-score, with a clear ability to translate them into business ROI such as reduced false-positive fraud blocks or manual review cost savings.
•Strong ability to evaluate technical trade-offs between real-time inference and batch scoring, feature compute complexity and latency SLAs, and model accuracy versus explainability.
•Deep understanding of regulatory constraints around automated financial decisioning, model transparency, data privacy under GDPR, and audit trails in regulated environments.
•Ability to abstract domain-specific requirements into reusable horizontal ML services consumed by multiple engineering squads.
•Comfortable navigating ambiguity, making high-stakes technical trade-off decisions, and prioritizing foundational infrastructure investments alongside feature delivery.
•High bar for system resilience, monitoring, and fail-safe mechanisms, recognizing that core banking engines demand 99.99% reliability.
Conditions
•Employee benefits including a competitive personal development budget, work from home budget, fitness & wellness discounts, and public transportation subsidies.
•A Premium N26 bank account subscription for you, plus subscriptions for friends and family.
•An additional day of annual leave for each year of service.
•High autonomy to drive the ML technical agenda, working with a diverse, international team of peers.
•A relocation package with visa support for those who need it.
