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 (España)
🏢 N26
📍 España