Wise is a global technology company, building the best way to move and manage the world’s money.
About the role
We’re looking for a Lead Data Scientist (IC3) to join our Anti-Money Laundering (AML) Handling & Prevent team in London.
You'll help automate components of our operational systems, establish robust LLM evaluation pipelines, and build solutions that reduce financial crime risk.
Responsibilities
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), targeting 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 measure accuracy, precision, recall, and failure modes.
Prompt Optimization & Experimentation: Audit existing prompts and run structured experiments (few-shot, chain-of-thought, context ordering) to reduce hallucinations, formatting errors, and prompt drift.
Shadow Testing & Monitoring: Design shadow mode deployments and parallel execution testing, implementing post-deployment monitoring for data and output drift.
Full-Stack Deployment: Own the production pipeline by writing and deploying production-ready Python services.
Human-in-the-Loop Architecture: Design systems where AI provides recommendations and drafts, ensuring human operators retain final decision-making authority.
Strategic Demand Deflection: Analyze upstream data to create strategies that deflect financial crime attempts.
Technical Leadership & Mentorship: Set technical direction, mentor Senior and Junior Data Scientists, foster a product-focused mindset.
Requirements
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.