Lead Data Scientist - Agentic AI / AML

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About This Role

Wise is hiring a Lead Data Scientist (IC3) to join the Anti-Money Laundering (AML) Handling & Prevent team based in London. This hybrid role centers on developing artificial intelligence and intelligence systems to automate operational reviews and mitigate financial crime risks. The successful candidate will architect robust AI systems, create evaluation pipelines, and deploy Python services to support business and consumer enhanced due diligence workflows. Responsibilities include building agentic AI…

Job Description

Responsibilities

• Develop and deploy artificial intelligence models to augment operational workflows such as consumer and business enhanced due diligence
• Establish labeling taxonomies and automated evaluation harnesses to measure accuracy, precision, recall, and failure modes
• Audit existing prompts and run structured experiments using hypothesis-driven frameworks to reduce hallucinations and prompt drift
• Design multi-step agentic AI workflows that reason across contextual information and tools reliably
• Construct shadow mode deployments and parallel execution testing for large-scale evaluation
• Write and deploy production-ready Python services to handle the production pipeline
• Create human-in-the-loop system architectures where artificial intelligence provides drafts and recommendations for critical assessments
• Analyze upstream data to build demand deflection strategies that prevent financial crime attempts before reaching operations staff
• Set technical direction and mentor senior and junior data scientists

Requirements

• Experience in implementing, training, testing, and evaluating the performance of machine learning systems
• Strong proficiency in Python
• Hands-on experience developing or working with generative artificial intelligence or large language model solutions
• Familiarity with prompt engineering, evaluation techniques, and large language model-powered workflows
• Experience with statistical analysis, experiment design, and failure mode analysis
• Willingness to read historical operational cases

Qualifications

• Proven familiarity and experience with object-oriented programming principles
• Experience with fine-tuning, reinforced learning alignment, and evaluation techniques in a training pipeline
• Familiarity with agentic frameworks such as LangGraph
• Experience designing tool-using agents, orchestration workflows, or human-in-the-loop systems
• Working knowledge of Java

Core Skills

Benefits

• Stock options in a profitable company
• Hybrid working model with flexibility for remote work, overseas work, and personal life administration
• Annual personal development budget for books, courses, or conferences
• Visa and relocation support

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