4 of 4 Fraud Detection Jobs in the North West

business challenges, design and implement data-driven solutions, and provide actionable insights that drive business value. Your ability to address challenges specific to financial services, such as risk modeling, fraud detection, and regulatory compliance, will be a critical asset. #LI-DNI Responsibilities Support financial services clients with the definition and implementation of their AI strategy, focusing on areas … an emphasis on regulatory compliance (e.g., Basel III, GDPR) and ethical AI principles Ideate, design and implement AI-enabled solutions for financial services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments … MiFID II or the EU AI regulatory framework Deep understanding of LLMs and their application in areas like financial document analysis, customer service chatbots or regulatory reporting Expertise in fraud detection techniques, anomaly detection and compliance analytics Strong understanding of ML Ops principles and experience in deploying and managing AI/ML models in financial systems Proficiency More ❯

your own projects, drive modelling initiatives, and take ideas from concept to production You'll be encouraged to propose new approaches and explore creative ways to detect and prevent fraud We debate and critique our ideas in a healthy, supportive team You'll have the chance to shape both models and how we think about fraud detection … that builds, evaluates and deploys machine learning models to improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning … warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the level of fairness and bias machine learning models Enthusiasm for improving fraud detection systems and a proactive, problem-solving mindset Interview process Interviewing is a two way process and we want you to have the time and opportunity to get More ❯

your own projects, drive modelling initiatives, and take ideas from concept to production You’ll be encouraged to propose new approaches and explore creative ways to detect and prevent fraud We debate and critique our ideas in a healthy, supportive team You’ll have the chance to shape both models and how we think about fraud detection … that builds, evaluates and deploys machine learning models to improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning … warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the level of fairness and bias machine learning models Enthusiasm for improving fraud detection systems and a proactive, problem-solving mindset Interview process Interviewing is a two way process and we want you to have the time and opportunity to get More ❯

to-consumer. Our client portfolio features globally recognized brands such as Coca-Cola, Nestlé, Elemis, Homebase, and Procter & Gamble. Role Overview The ML Manager (Operations) leads multiple squads covering Fraud, Customer Experience (CX), and Logistics. You will oversee a cross-functional team focused on leveraging machine learning to enhance business operations, including developing AI-driven solutions for fraud detection, customer support, and logistics optimization. Key Responsibilities Lead, mentor, and manage a team of machine learning professionals. Define and execute the machine learning strategy aligned with business goals. Oversee the entire lifecycle of ML projects from conception to deployment and monitoring. Guide the team in building, training, and deploying models. Ensure best practices in data preparation, feature More ❯