As a Senior Data Science/ML Engineer in the Risk AI Engineering Squad, you will build the production systems that turn machine learning into reliable, explainable transaction-monitoring capabilities. You will work across the full model lifecycle: understanding financial-crime typologies, exploring data, engineering features, training and validating models, deploying them at scale, and monitoring their performance over time.

This role is designed for someone who is strongest on the engineering side of machine learning and wants to keep growing their data-science depth. You do not need to be a traditional data scientist or ML Engineer. We’re looking for someone who enjoys working across both disciplines: building robust, production-ready software while staying close to the data, models, and decisions those systems support.

You will join a cross-functional team within the Risk & Compliance tribe, working closely with AML and Fraud Operations, investigators, Product, and Engineering. Together, we build data products and ML solutions that help Risk teams work smarter, faster, and more effectively — while keeping our controls robust, auditable, and compliant across products and markets.

We actively welcome applications from women and people from underrepresented backgrounds. Diverse perspectives make our team stronger and our systems more robust. If you're motivated by technical depth, real-world impact, and the challenge of making ML work reliably in a high-stakes environment, this role is built for you.

What You’ll Do

Build ML systems that work in production

• Own and evolve end-to-end batch training pipelines for transaction-monitoring models.
• Build reliable software around the model lifecycle, including testing, CI/CD, versioning, deployment, monitoring, and rollback.
• Improve the maintainability, observability, and scalability of our model pipelines.
• Partner with platform and software engineers to make model delivery repeatable and safe.