Senior Data Science Lead - AML Risk

Description

You would lead the data science team in the AML Risk group in London, which owns anti-money laundering detection across every licence the company holds.

The role involves building out the lead data science function and the machine learning solutions for this group.

The company is a global technology business that moves and manages money internationally. The team's systems help keep customers safe and keep bad actors out of the ecosystem at scale.

The team combines unsupervised and supervised learning with GenAI to detect and reduce financial crime worldwide.

You would join a product team that already runs but is still growing. Your work affects millions of customers.

Responsibilities

• Build AML detection controls that mix unsupervised, semi-supervised and supervised learning with GenAI.
• Create ways to show that these controls cover each region.
• Build technology that serves the company's users across many countries.
• Set staffing needs with product managers and engineering leads, then hire specialists to meet them.
• Coach more junior team members on technical and non-technical skills.
• Test AML systems against internal and external benchmarks.
• Build decisioning layers that balance precision against recall.
• Forecast likely outcomes under different scenarios using data.
• Work with operations teams to refine their processes and feed their feedback into automation.
• Run projects that use spare operational capacity, such as manual data labelling to improve models.
• Build systems that give investigators detailed insight into red flags and typologies on profiles and transactions.
• Package algorithms as deployable libraries or objects and move them from staging to production.
• Set up and maintain scheduled data gathering and model retraining in automated pipelines.
• Maintain production-grade Python services.

Requirements

• Hands-on experience implementing, training, testing and assessing machine learning systems.
• Strong Python skills.
• Experience in statistical analysis and designing well-structured experiments.
• A strong product mindset and the ability to work independently across functions and teams.
• Clear communication, including explaining ideas to non-technical people.
• Strong problem solving, including helping to sharpen unclear problem statements and working out how to solve them.
• Great to have (but not essential):
• A big plus: proven familiarity and experience with OOP principles.
• Familiarity with automating operational processes through technical tools such as Large Language Models.
• Willingness to work hands-on alongside operations staff to understand their pain points.
• Knowledge of or experience in the financial crime domain.

Conditions

The role is based in London.

The team does not judge people by background and especially welcomes applications from under-represented groups, valuing experience and clear thinking over formal qualifications.