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 lives 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 our mission and what we offer.
Job DescriptionLead 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 AI and intelligence systems at the core of our operational handling and prevention work. You'll help build and automate components of our operational systems using Generative AI, LLMs and agentic AI approaches, 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, Agentic AI and machine learning architectures. This team is dedicated to enhancing our financial crime mitigation operations through advanced tooling, automated evaluation frameworks, prompt optimisation and increasingly autonomous AI workflows, 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 AI 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, 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 (few-shot, chain-of-thought, context ordering) 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. 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;
Some hands-on experience developing or working with GenAI / LLM solutions. You don’t need to be an LLM specialist, but you should have professional exposure to areas such as prompt engineering, evaluation, LLM-powered workflows or Agentic AI;
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.
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 InformationOffice: London
Key benefits:
Stock options in a profitable company
Hybrid working model - whether it’s working from home, working overseas, school plays or life admin we get that flexibility is essential
Annual personal development budget - whether it’s for books, courses, or conferences
Visa and relocation support
You can read more about our full benefits package here...
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 Wise.Jobs.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Wise London, England Office
Wise London Office
Our central London office at Worship Square in Shoreditch is a dynamic, full-stack hub. Teams across Engineering, Product, and Operations work side by side to design, build, and ship products globally. Wise offers a rare level of trust and ownership, accelerating your career impact.
