The opportunity
We recognize that our strength lies in our people and the varied perspectives they bring to our workforce. We strive to build talented and diverse teams to drive our business success and empower our people to reach their full potential.
What you'll do
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.
What they're looking for
- + 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.
- 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.
