Lead Data Scientist - Sanctions Screening

Wise
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist / Deep Learning Practitioner

Lead Data Scientist / Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist / Deep Learning Practitioner

Company Description

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 life 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 .

Job Description

About the Role: 

Our screening team is responsible for sanctions, PEPs (Politically Exposed Persons) and Adverse Media screening. 

The screening team has a name matching service that is routinely tested against an internal benchmarking suite, and annually against external benchmarking suites. 

We are looking for someone to own the testing, tuning and optimisation of these matching algorithms. The candidate will own this function - be responsible for the overall testing and tuning strategy, deep dive testing results, understanding how to optimise rules for efficiency and effectiveness and create rules for complex controls.

Here’s how you’ll be contributing:

Automatising algorithm testing on real customer data as well as synthetic data. Packaging the service into a library or deploying it to staging or production environments

Benchmark testing

Analyse results from exact name matching and fuzzy name matching against internal and external benchmarks

Identify and categorise types of missed cases and map those to known or new issues

Propose technical solutions to reduce the number of missed cases identified

Benchmark creation

Design an internal test set to evaluate both the precision and recall of the screening engine

Align performance on the test set with real-life production performance

Extend internal benchmarking to new scenarios for better coverage of screening algorithms

Define an overall strategy for testing and tuning

Evaluate the use of an internal benchmarking tool 

Evaluate testing capabilities of external vendors in the market to define the most effective method of continuous external benchmarking, processes of governance 

Tailor business rules to reduce hit rate, prepare tuning data, reviewing hit reduction strategies and work with product managers, compliance and engineers to ensure roadmap alignment

Rule optimisation

Tweaking engine configuration to find the sweet spot for precision and recall.

Provide answers on how many historical true positives we would miss based on different optimisations

Work on advanced rules for complicated controls, such as vessel screening
 

Uncover and action on opportunities to help the Screening operational team scale

Automatising or creating solutions to assist operations in their work

Modify existing tooling introducing LLM assistants to improve the efficiency of agents and speed of case resolution

A bit about you: 

Experience implementing, testing and evaluating performance of multiple rules across systems;

Strong Python knowledge. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services;

Strong algorithmic design and testing skills. A big plus for proven experience with name matching algorithms;

Experience with statistical analysis and good presentation skills to drive insight into action;

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 through technical solution, for example Large Language Models;

Knowledge of Sanctions and Name Screening Optimisation and Tuning

Experience working in a heavily regulated business domain.

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.

Additional Information

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 .

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

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.