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N26

Mobile-first digital bank with real-time payments

Lead Technical Product Manager - Machine Learning Platform

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeBerlin, GermanyNo salary listedIn-person

About the Job

Requirements

  • At least 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.
  • A proven track record of shipping ML products in high-concurrency, low-latency environments such as FinTech, payment processing, or high-scale consumer technology 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 Machine Learning 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.
  • High proficiency in defining model performance metrics such as precision, recall, ROC-AUC, and F1-score, and translating them into business ROI such as reduced false-positive fraud blocks or manual review cost savings.
  • Ability to evaluate technical trade-offs between real-time inference and batch scoring, feature-compute complexity and latency service-level agreements, 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 used by multiple engineering squads.
  • Ability to make high-stakes technical trade-off decisions and prioritize foundational infrastructure investments alongside feature delivery.
  • High standards for system resilience, monitoring, and fail-safe mechanisms, recognizing that core banking engines demand 99.99% reliability.

Responsibilities

  • Define and execute the multi-year roadmap for the central Machine Learning platform, aligning it with broader engineering architecture and executive business goals.
  • Oversee complex predictive ML capabilities supporting high-throughput banking systems, including real-time transaction monitoring, automated credit-risk scoring, and operational workflow decisioning.
  • Own platform tooling and standards across the full ML lifecycle, including strategic decisions about 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 such as 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-organization best practices, PRD standards, and self-service ML integration blueprints, and mentor Product Managers in the team.

About the Company

N26

Mobile-first digital bank with real-time payments

N26 provides real-time digital banking services for individuals and businesses, with accounts, payment solutions, and mobile access. It operates primarily in Europe and the United States, offering standard and premium consumer accounts as well as business accounts, plus payment options like Mastercard, Apple Pay, and Google Pay. The platform supports instant payments, automatic transaction notifications, and savings management via Spaces, a feature for setting and tracking savings goals. Revenue comes from subscription fees for premium accounts, card interchange fees, and partnerships with other financial providers. The goal is to offer a flexible, mobile-first banking experience that helps tech-savvy users and small businesses manage money on the go.

Company size
1,001-5,000
Company stage
Series E
Total funding
$1.7B
Headquarters
Berlin, Germany
Founded
2013

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