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N26 is seeking a Technical Product Manager for Applied Machine Learning to join the Intelligent Operations Platforms (IOP) segment. You will build and deploy machine learning solutions that empower internal teams and enable N26 to scale operations efficiently and securely.
In this role, you will execute the ML product roadmap in partnership with senior and lead product managers, owning feature delivery from discovery through deployment. You will act as the day-to-day connector between data scientists, ML engineers, and business stakeholders, translating operational pain points into clear technical specifications and user stories. Your focus will be on delivering predictive models that solve concrete internal bottlenecks—automating back-office tasks, accelerating document verification, and improving fraud detection accuracy.
You will define, track, and analyze performance metrics for live models, monitoring production accuracy, latency, and false positive rates to identify opportunities for continuous retraining and optimization. You will partner with engineering leads to groom product backlogs, write detailed user stories, and participate in daily sprint rituals, ensuring steady delivery while upholding strict security standards.
N26 is a digital banking platform headquartered in Berlin with offices across Europe, including Vienna and Barcelona, serving a 1,500-person team of more than 80 nationalities. The company has reimagined banking for the digital world, eliminating physical branches, paperwork, and hidden fees for an elegant digital experience.
Benefits include a competitive personal development budget, work-from-home budget, fitness and wellness memberships, language apps, public transportation support, a Premium N26 bank account subscription, additional annual leave accrual, high autonomy, access to cutting-edge technologies, and a relocation package with visa support for those who need it.
REQUIREMENTS:
- 4+ years of experience as a Product Manager in a technology-driven environment
- Hands-on experience with, or very strong interest in, data products, analytics, or machine learning workflows
- Data fluency and SQL: highly comfortable querying and analyzing datasets independently; strong ability to use SQL and data visualization platforms to evaluate model impact and validate hypotheses
- Core ML fundamentals: solid understanding of basic machine learning concepts including classification, regression, decision trees, and the standard model lifecycle from data collection to deployment
- Model evaluation understanding: ability to translate technical metrics (precision, recall, F1 score) into practical operational outcomes
- Technical communication: skill in translating complex business rules into precise requirements for engineers and explaining model outputs to operational teams
- Execution bias: focus on delivering pragmatic, incremental value quickly
- Analytical problem-solving: curiosity about complex backend processes and drive to uncover operational friction
- Banking quality standard: respect for security, compliance, and data privacy requirements in regulated financial institutions
