Senior Data Science Lead - AML Risk

We’re looking for a Data Science Lead to join our AML Risk team in London.  This role is a unique opportunity to work on building out the lead Data Science team and machine learning based technical solutions in the AML Risk team, which owns AML detection across all of the Wise licenses. This is an exciting opportunity to develop the program in a global company. 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 making sure the AML Risk Data Science team is well equipped and working on cutting-edge technology to sustainably support Wise’s growing customer, transaction and product space. You will be stepping into an already functioning, but growing product team.   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   Building a team of high performing specialists Working with product managers and engineering leads to understand staffing requirements Hiring specialists 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. A big plus for proven familiarity and experience with OOP principles; …