About the opportunity
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.
Our ML Platforms team serves as the central nervous system for real-time, data-driven decisioning. We build and scale horizontal ML capabilities that allow critical functions including Financial Crime, Credit Risk, and Core Operations to execute with absolute precision at scale. As a Lead Product Manager, you will drive multi-squad strategy, transforming complex predictive models into resilient, compliant, sub-second scoring systems.
In this role, you will
- Drive the Core ML Platform Vision: Define and execute the multi-year roadmap for the central Machine Learning platform, ensuring alignment with broader engineering architecture and executive business goals.
- Lead High-Volume ML Portfolios: Oversee complex predictive ML capabilities supporting high-throughput banking systems, focusing on real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning.
- Scale Enterprise MLOps Infrastructure: 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.
- Establish Model Evaluation and Governance: 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.
- Bridge Technical Leadership and Compliance: Partner closely with Principal Data Scientists, ML Engineers, Security, and Legal stakeholders to ensure models meet strict European banking governance, explainability, and auditability standards.
- Elevate and Mentor the PM Function: 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.
Who we are
N26 has reimagined banking for today’s digital world. Technology and design empower everything we do and it’s how we are building the global banking platform the world loves to use.
We've eliminated physical branches, paperwork, and hidden fees for an elegant digital experience and supreme savings. Giving people the power to live and bank their way is what gets us out of bed in the morning and inspires the work that we do.
We are headquartered in Berlin with offices in multiple cities across Europe, including Vienna and Barcelona, and a 1,500 strong team of more than 80 nationalities.
What’s in it for you
- Accelerate your career growth by leading core infrastructure at one of Europe’s premier digital banks.
- 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.
Background & Experience
- 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.
- High-Volume B2C Scale: 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.
- Strategic & Portfolio Leadership: Demonstrated experience leading multi-squad initiatives, shaping platform strategy across cross-functional engineering triads, and presenting to C-level executives and regulatory bodies.
Technical & Domain Skills
- Deep ML and MLOps Literacy: Hands-on understanding of predictive ML methodologies including classification, regression, clustering, anomaly detection, and ranking. This must be paired with architectural mastery of modern MLOps pipelines such as Kafka event streams, feature stores, vector databases, and automated retraining.
- Analytical and Evaluation Rigor: 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.
- Latency and System Trade-Offs: 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.
- Financial Governance and Compliance: Deep understanding of regulatory constraints around automated financial decisioning, model transparency, data privacy under GDPR, and audit trails in regulated environments.
Traits
- Platform Visionary: Ability to abstract domain-specific requirements into reusable horizontal ML services consumed by multiple engineering squads.
- Decisive Technical Leader: Comfortable navigating ambiguity, making high-stakes technical trade-off decisions, and prioritizing foundational infrastructure investments alongside feature delivery.
- Uncompromising Quality Standard: High bar for system resilience, monitoring, and fail-safe mechanisms, recognizing that core banking engines demand 99.99% reliability.
