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

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;

• Experience with statistical analysis, and ability to produce well-designed experiments;

• A strong product mindset with the ability to work independently in a cross-functional and cross-team environment;

• Good communication skills and ability to get the point across to non-technical individuals;

• Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them.

Some extra skills that are great (but not essential):

• Familiarity with automating operational processes via technical solutions, for example Large Language Models

• Willingness to get hands dirty with operational side by sides to understand their pain points

• Knowledge and experience within the Financial Crime domain