Lead Data Scientist - AML Handling

Lead Data Scientist - Anti-Money Laundering (AML) Handling & Prevent We’re looking for a Lead Data Scientist (IC3) to join our Anti-Money Laundering (AML) Handling & Prevent team in London. This role is a unique opportunity to work on the intelligence system at the core of our operational handling and prevention work. You'll help automate components of our operational systems, establish robust LLM evaluation pipelines, and build solutions that reduce financial crime risk. What you build will have a direct impact on Wise’s mission and millions of our customers.

The AML Handling & Prevent team offers an exciting environment for applying cutting-edge Generative AI solutions and machine learning architectures. This team is dedicated to enhancing our financial crime mitigation operations through advanced tooling, automated evaluation frameworks, and prompt optimization, aiming to streamline reviews and simplify the work of our operations staff. As a Lead Data Scientist (IC3), you will drive technical strategy across handling and prevent initiatives, architect robust AI systems, establish post-deployment monitoring, and lead complex automation initiatives to reduce financial crime risk across Wise.

Here’s how you’ll be contributing

• End-to-End Automation & EDD LLMs: Lead the development and deployment of AI models designed to augment operational workflows (e.g. Business and Consumer EDD LLMs), specifically targeting the automation of case summaries, red flag generation, risk classifications, and document requests.
• Evaluation Framework & Labeling Taxonomy: Establish labeling taxonomies and guidelines with EDD SMEs, construct evaluation datasets, and implement automated eval harnesses to systematically measure accuracy, precision, recall, and failure modes.
• Prompt Optimization & Experimentation: Audit existing prompts and run structured experiments (few-shot, chain-of-thought, context ordering) within a hypothesis-driven testing framework to reduce hallucinations, formatting errors, and prompt drift.
• Shadow Testing & Monitoring: Design shadow mode deployments and parallel execution testing to safely evaluate prompts at scale, while implementing post-deployment monitoring for data and output drift.
• Full-Stack Deployment: Take ownership of the production pipeline by writing and deploying production-ready Python services. You must be willing to bypass engineering bottlenecks to ship value quickly while maintaining code quality.
• Human-in-the-Loop Architecture: Design systems where AI provides recommendations and drafts, ensuring human operators retain the final decision-making authority for critical financial crime mitigation assessments.
• Strategic Demand Deflection: Go beyond ticket handling by analyzing upstream data to create strategies that deflect financial crime attempts before they reach the operations team, effectively reducing manual workload.
• Technical Leadership & Mentorship: Set technical direction, mentor Senior and Junior Data Scientists, foster a product-focused mindset, and guide the team through complex technical implementations, architectural decisions, and AI governance standards.

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;
• Knowledge and experience developing and evaluating GenAI / LLM solutions, including automated eval harnesses and prompt engineering;
• Experience with statistical analysis, experiment design, and failure mode analysis in LLM systems;
• 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 and SMEs;
• 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
• Experience implementing fine-tuning, reinforced learning alignment and evaluation techniques within an LLM training pipeline.
• Familiarity with agentic frameworks such as LangGraph or similar.
• Willingness to get hands dirty reading many, many historical operational cases.
• Working knowledge of Java.

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.