About the role
Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot , based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here ). Our journey: Founded in 2017 by Alexandre and Steve , Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's
who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in
how we work ( here ) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ Join us as a Staff Product Manager - Data to enhance Qonto's customer experience through innovative ML solutions. You'll drive the maturity and scalability of our ML capabilities, addressing concrete business problems while fostering innovation. You will work closely with Daniele Garbarino , our VP Data, to support ML products developments that significantly impact our 600,000+ customers' financial management experience. ➡️
What you'll do Own the ML product roadmap end-to-end: Define what we build, why, and in what order — gathering insights from engineering, data science, and business teams to develop a prioritized strategy that addresses real customer needs. Drive key ML product initiatives: Lead the definition and design of ML solutions for products like Attachment Matcher, Transaction Categorizer, and Cashflow Management — from discovery through delivery. Collaborate across the full stack: Work with data scientists, engineers, and business stakeholders to ensure successful implementation of ML capabilities that drive measurable business value. Own your decisions and communicate them clearly: Articulate the rationale behind priorities and engage key stakeholders — including customers, product, and executive teams. Continuously improve through data: Monitor and analyze the performance of ML models and products, making data-driven recommendations to raise the bar. ➡️