Data Scientist, CIB Business Risk

Some careers shine brighter than others. If you’re looking for a career that will help you stand out, join HSBC and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.  HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions. We are currently seeking an experienced professional to join our team in the role of  Data Scientist - Business Risk In this role, you will: Act as a fraud analytics SME and trusted advisor for CIB Business Risk, supporting Fraud Risk Management within the Merchant Card Acquiring business across one or more regions. Partner with Acquiring business teams, Operations, Technology, Product, Compliance and Financial Crime Risk to drive data-led decisions that reduce fraud losses while protecting merchant and customer experience. Maintain a strong understanding of the merchant acquiring lifecycle (onboarding, underwriting, transaction processing, settlement, and disputes/chargebacks) and the key fraud risks and control points across each stage. Develop and support analytics strategies aligned to business priorities, including improved detection effectiveness, reduced false positives, and stronger control performance. Create reusable and scalable analytical assets (typologies, features, rules, model components, and monitoring packs) that can be deployed consistently across markets and merchant segments. Communicate complex analytical findings in a clear, executive-ready format, enabling timely decisions and effective governance, while consistently upholding HSBC Core Values. Key focus areas: Merchant Acquiring fraud analytics. Design and implement fraud typologies across the merchant lifecycle, including: Onboarding and underwriting (e.g., suspicious applications, high-risk profiles). Early-life monitoring (e.g., rapid spikes in activity, abnormal refunds/voids) Portfolio monitoring (e.g., behavioural drift, emerging patterns, peer outliers). Build and operationalise detection approaches such as: Anomaly detection for volume, value, velocity, geography, refund ratios, and chargeback rates. Rules and scorecards to support near-real-time monitoring and triage. Network/graph analytics to identify linked merchants and suspicious relationship clusters. Segmentation and peer benchmarking by MCC, channel (e-commerce vs card-present), region, and merchant size. Support priority fraud themes including CNP fraud patterns, refund/chargeback abuse, and indicators of compromised merchants, ensuring solutions are practical for operational adoption. Data, technology, and delivery expectations …