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