Lead Data Scientist - AML

Skills this role screens for

The work

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.

Also

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

Who they want

A bit about you

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

We’re people without borders — without judgement or prejudice, too. We want to work with the best people, no matter their background. So if you’re passionate about learning new things and keen to join our mission, you’ll fit right in.

Also, qualifications aren’t that important to us. If you’ve got great experience, and you’re great at articulating your thinking, we’d like to hear from you.

And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under-represented demographic.

For everyone, everywhere. We're people building money without borders  — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.

Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Keep up to date with life at Wise by following us on LinkedIn and Instagram.

• 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.
• 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