Lead Data Scientist - Agentic AI / AML

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

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 AI and intelligence systems at the core of our operational handling and prevention work.

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 AI Automation & EDD LLMs: Lead the development and deployment of AI models designed to augment operational workflows, specifically targeting the automation of case summaries, red flag generation, risk classifications, document requests and agentic workflows.
• 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 within a hypothesis-driven testing framework to reduce hallucinations, formatting errors, and prompt drift.
• Agentic AI Systems: Help design and develop AI workflows that can reason across multiple steps, use tools and contextual information, and support complex operational processes safely and reliably.
• Shadow Testing & Monitoring: Design shadow mode deployments and parallel execution testing to safely evaluate prompts and AI workflows 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.
• 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;
• Some hands-on experience developing or working with GenAI / LLM solutions;
• 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 ML or 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, AI agents or multi-step AI workflows;
• Experience implementing fine-tuning, reinforced learning alignment and evaluation techniques within an LLM training pipeline;
• Familiarity with agentic frameworks such as LangGraph or similar;
• Experience designing tool-using agents, orchestration workflows or human-in-the-loop AI systems;
• Willingness to get hands dirty reading many, many historical operational cases;
• Working knowledge of Java.

Key benefits:

• Stock options in a profitable company;
• Hybrid working model;
• Annual personal development budget;
• Visa and relocation support.