# Lead Data Scientist - Agentic AI / AML

**Company**: Wise
**Location**: London
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Data Science
**Industry**: Technology

**Apply**: https://jobs.smartrecruiters.com/Wise/744000159590339-lead-data-scientist-agentic-ai-aml-?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/job/lead-data-scientist-agentic-ai-aml-a156ec26-295/

## Description

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.

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## Skills

### Required
- Machine Learning
- Python
- GenAI
- LLM
- prompt engineering
- statistical analysis
- experiment design
- failure mode analysis

### Nice to have
- Large Language Models
- AI agents
- multi-step AI workflows
- fine-tuning
- reinforced learning alignment
- evaluation techniques
- agentic frameworks
- LangGraph
- tool-using agents
- orchestration workflows
- human-in-the-loop AI systems
- Java

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