Broadgate Search is partnering with a well-established financial data and analytics business.
They’re hiring a Director of Fraud & Credit Risk Analytics to lead the analytical solutions that banks, lenders and fintechs use to manage fraud, payment and credit risk.
This is a hands-on leadership role at the point where data science, product and the client meet. You’d build and lead the fraud risk analytics team, take new risk solutions from concept to live deployment, and act as the go-to subject-matter expert in front of clients and the wider market.
What you’ll be doing
? Analytical leadership: Lead work across first- and third-party fraud, identity/application fraud, ATO, scams, synthetic ID, payment and return risk, credit underwriting and portfolio monitoring.
? Solution development: Turn client problems into clear use cases, data requirements and solution designs. Own the full lifecycle: problem definition, labels, features, model and strategy design, validation and implementation readiness.
? Performance frameworks: Define how success is measured, including detection rates, false positives, approvals and declines, customer friction, credit losses, lift and stability.
? Implementation: Advise clients on putting solutions into onboarding, account opening, payments, authorisation and ongoing monitoring workflows. This covers thresholds, score interpretation, fallback logic and post-deployment monitoring.
? Client and market facing: Lead POCs and solution evaluations, support Sales and RFPs, and represent the business at executive briefings, conferences and webinars.
? Team and cross-functional leadership: Develop the team, set analytical standards and governance, and shape product strategy alongside Product, Engineering, Sales, Compliance and Operations.
What you’ll bring
?? 8+ years of relevant experience, including 5+ years leading analytics teams that deliver production-ready solutions
?? Deep domain expertise in fraud, payment risk, credit risk or financial crime, and a strong grasp of how risk operations and regulation work at financial institutions
?? Strong hands-on skills in feature engineering, statistical modelling and machine learning on large, complex datasets
?? Strong Python and SQL, with R a plus
?? A master’s degree (or equivalent) in data science, statistics, computer science or another quantitative field
?? Confidence turning business and client needs into scalable, measurable solutions, and explaining complex results to technical and commercial audiences alike
