OverviewAs Lead Technical Product Manager for the ML Platform at N26, you define the long-term vision and architecture for core ML infrastructure.

You will align platform strategy with engineering and executive goals, enabling squads to automate risk assessments, prevent financial crime, and scale operations.

You design high-throughput, real-time ML capabilities and governance to support Financial Crime, Credit Risk, and Core Operations.

You lead multi-squad initiatives, shaping resilient, compliant, sub-second scoring systems that drive business outcomes.

Compensaciones / Beneficiospersonal development budget work from home budget fitness & wellness discounts public transportation subsidies premium N26 bank account subscription relocation package with visa support

ResponsabilidadesDefine and execute the multi-year roadmap for the central ML platform, aligning with engineering architecture and executive goals

Oversee high-volume ML capabilities supporting real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning

Scale enterprise MLOps infrastructure, including feature stores, real-time feature extraction, model registries, retraining triggers, and ML CI/CD

Establish robust evaluation and governance, including offline/online evaluation, shadow deployment (champion/challenger), and real-time monitoring for drift, latency, and data quality

Bridge technical leadership and compliance by partnering with data scientists, ML engineers, Security,



and Legal to meet EU banking governance and auditability standards

Elevate the PM function by establishing best practices, PRD standards, and self-service ML integration blueprints while mentoring Product Managers

Requisitos principales7+ years of Product Management experience in a technology-driven environment at least 4 years focused on building, scaling, and managing core ML platform capabilities or data infrastructure experience shipping ML products in high-concurrency, low-latency environments (FinTech, payments, or high-scale consumer tech)

experience in leading multi-squad initiatives and presenting to C-level executives and regulatory bodies hands-on understanding of predictive ML methodologies and modern MLOps pipelines (e.g., feature stores, Kafka streams, vector databases, automated retraining)

strong ability to define model performance metrics and translate them into ROI ability to evaluate real-time vs batch scoring trade-offs and explainability considerations deep understanding of GDPR, data privacy, and audit trails in regulated environments platform vision and strategic thinking decisive technical leadership under ambiguity high standards for reliability, monitoring, and security predictive ML methodologies (classification, regression, clustering, anomaly detection, ranking)

MLOps pipelines (feature stores, real-time feature extraction, model registries)

real-time and batch inference latency trade-offs

📌 Lead Technical Product Manager — Machine Learning Platform (Barcelona)
🏢 N26
📍 Barcelona