Lead Data Scientist - AML

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