UK remote
Lead Data Scientist - Anti-Money Laundering (AML)
About this role
Lead Data Scientist- Anti-Money Laundering Team We’re looking for a Lead Data Scientist to join our AML team in London. As a Lead Data Scientist in the AML team you will have the exciting opportunities to build AI based financial crime detection systems aimed at keeping our customers safe across the globe. Your work will allow Wise to keep our customers safe and making sure we can keep our ecosystem free of bad actors in a scalable way.
What you build will have a direct impact on Wise’s mission and millions of our customers. About the Role: In the Anti-Money Laundering (AML) Risk team we are developing systems which are a mixture of unsupervised and supervised learning, with GenAI to detect and mitigate Financial Crime on a global scale. You will be working on cutting-edge technology to sustainably support Wise’s growing customer, transaction and product space.
Here’s how you’ll be contributing: AML Risk Detection System Development Developing efficient and effective AML detection controls using a mixture of unsupervised, semi-supervised and supervised learning with GenAI Creating frameworks to prove controls coverage at a regional level Developing technologies to serve Wise’s diverse international user base Working in Cross-Functional Teams Working across functions to own and solve AML related problems at a global scale Mentoring more junior members of the team on technical and non-technical skillsets Performance Testing and Optimisation Evaluating our AML systems against internal and external benchmarks Developing decisioning layers to find optimal trade-offs between precision and recall Providing data-driven insights on potential outcomes under various scenarios Operational Process Development Collaborating with operational teams to refine processes, ensuring effective feedback integration into our automation systems Designing and managing projects that utilise excess operational capacity, such as manual data labelling for model improvement Creating systems which provide in-depth insight to investigators on red flags and typologies present on profiles/transactions Deployment and Implementation Packaging algorithms into deployable libraries/objects and transitioning them from staging to production environments Implementing and maintaining scheduled processes for data gathering and model retraining using automated pipelines Maintaining production-grade Python services A bit about you: Experience implementing, training, testing and evaluating performance of Machine Learning systems; Strong Python knowledge.