Top AI & Machine Learning Jobs in London

Lead development of the label integrity layer for Risk ML: ensure label quality, define statistical metrics, build automated audits, monitor label drift, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Build and maintain the integrity layer for label data used in FinCrime ML models. Define and monitor statistical quality metrics, design automated label audit processes, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Lead ML Engineer to build ML experimentation tooling and label quality for Fincrime/KYC/Support squads. Own roadmap, stakeholder management, delivery, and onboarding. Work across MLOps, distributed data processing, Terraform/AWS infra, CI/CD, monitoring, and ML governance; mentor junior team members and run PoCs to production.

Lead the development and lifecycle management of ML models for KYC and document verification. Build and maintain Python codebases, manage large image and tabular datasets, iterate on deep learning image models, measure and optimise model performance, research improvements, and mentor junior data scientists while collaborating with cross-functional teams.

Lead the development and lifecycle management of KYC document verification ML systems: train, test, iterate and optimize deep learning image models; manage large image and tabular datasets; maintain Python codebase; collaborate cross-functionally; mentor junior data scientists; research and apply new ML techniques to reduce fraud and improve onboarding experience.

Lead development of an AI-powered assistant and route-by-route activation strategy to convert first-time cross-border senders. Build scalable frameworks, run data-driven experimentation (A/B tests, funnels, cohorts), partner with engineering, compliance, pricing and regional teams, and guide designers and analysts to deliver localized, conversational transfer experiences that increase conversions and reduce customer effort.

Lead development and deployment of ML systems (neural nets, graph models, Transformers) for AML detection. Mentor team members, integrate models with production platforms and LLM agents, design data strategies, run large-scale training and evaluation, and communicate results to stakeholders.

Lead and build Wise's Risk Modelling ML engineering pillar: design a Model Factory and experimentation engine, implement model ops (retraining, drift detection, monitoring), hire and mentor senior ML engineers, set engineering standards, and partner with platform teams to safely scale hundreds of real-time financial crime models.

Lead Data Scientist for CRM/personalisation owning end-to-end production ML: feature pipelines, model training, inference and impact measurement. Build predictive, uplift, and recommendation models, design and analyse A/B tests, partner with analysts, engineers and CRM managers, and translate models into measurable CRM growth levers.

Lead Data Scientist responsible for designing, implementing, and deploying liquidity management and risk models. Work with Treasury, engineering, and product teams to forecast supply and demand, optimise cash placement, prepare stress scenarios, and present model results to stakeholders to keep liquidity risk within appetite.