Top AI & Machine Learning Jobs in London

Leads the Verification Data Science team within KYC and onboarding. Owns the data science roadmap, guides machine learning projects, manages image and tabular datasets, develops deep learning document-verification models, optimizes production performance, maintains Python code, researches fraud prevention, and coaches and hires data scientists.

Lead Data Scientist role at Wise focused on anti-money laundering initiatives. The provided description contains company information, mission, and inclusion statements but does not specify role responsibilities, qualifications, technologies, compensation, travel, or clearance requirements.

Leads the development and deployment of machine learning models for Wise’s Receive product, using customer and transaction data to improve risk management, prioritization, personalization, and customer outcomes. Designs experiments, builds production-grade data pipelines and modelling tools, analyzes trends and anomalies, and collaborates with engineering, product, analytics, operations, and risk teams. The role also supports scalable data science capabilities and fraud prevention across global markets.

Lead development and deployment of machine learning models that detect account takeover and send scams. Analyze transaction and event-log data, build real-time detection pipelines, design experiments, identify fraud patterns, and collaborate with engineering, security, and product teams. The role also involves mentoring junior data scientists, communicating insights to nontechnical stakeholders, and applying methods including anomaly detection, supervised and unsupervised learning, deep learning, graph-based solutions, and statistical analysis.

Lead data science initiatives for KYC and customer onboarding, developing and managing machine learning systems for document verification. Responsibilities include creating image and tabular datasets, iterating on deep learning models, optimizing model performance, maintaining Python code, researching fraud prevention methods, collaborating with stakeholders, and mentoring junior data scientists.

Lead the architecture and production deployment of deep learning and machine learning systems for real-time financial crime detection. Design sequence-based, graph-based, and attention-based models; establish reusable experimentation-to-production pipelines; evaluate foundation models and embeddings; make architecture decisions under latency and throughput constraints; partner with data scientists on evaluation and measurement; and mentor engineers and data scientists.

Lead development and deployment of machine learning and Generative AI systems for AML operations. Responsibilities include building EDD LLMs, evaluation datasets and automated testing frameworks, prompt optimization, shadow testing, production monitoring, human-in-the-loop workflows, and financial crime prevention solutions. The role also provides technical leadership, mentorship, architectural guidance, and cross-functional collaboration.

Lead data science for Wise’s AML Risk team by developing machine-learning and GenAI detection systems, optimizing precision-recall tradeoffs, building coverage frameworks, and deploying production Python services. Hire and mentor specialists, collaborate with product, engineering, and operations teams, design model evaluation experiments, improve operational processes, and create investigator insights for detecting financial crime across global transactions.

Develop and operate quantitative models and production systems for FX pricing, risk management, forecasting, hedging, and trading. Build Python-based quant libraries, services, data pipelines, and monitoring systems; conduct validation and backtesting; manage deployments and incident response; and collaborate with quants, traders, analysts, product managers, engineers, and risk teams.

Lead Wise’s Risk Modelling ML Engineering pillar for financial crime detection. Own the model lifecycle from experimentation through deployment, monitoring, retraining, drift detection, and auditability. Architect a scalable model factory and experimentation platform supporting hundreds of models under strict latency requirements. Recruit, mentor, and lead senior ML engineers while setting technical standards, making architecture decisions, and partnering with platform teams.