Data Science Lead - AML Risk

Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.

More about our mission and what we offer.

We’re looking for a Data Science Lead to join our AML Risk team in Tallinn. 

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;

• 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

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.

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