Data Scientist, 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
• Deliver robust ETL and data pipeline capabilities across acquiring transaction feeds, merchant master data, onboarding data, and disputes/chargebacks, ensuring strong data quality and integrity. Leverage cloud analytics platforms such as GCP, AWS (including Redshift), and/or Azure to support scalable delivery and deployment. Stakeholder engagement and collaboration.

To be successful you will:

• Degree-level qualification (or equivalent relevant experience) in Data Science, Computer Science, Statistics, Mathematics, Finance, or another quantitative discipline.
• 6+ years’ relevant experience, ideally within banking or the financial services sector. Strong grounding in advanced analytical methods such as regression, predictive modelling, data mining, and machine learning, with a structured and creative approach to problem solving.
• Proficiency in Python, SQL (or similar analytical programming languages), Alteryx, with experience in one or more of SAS, Spark, and Google Cloud Platform (GCP). Hands-on experience with cloud analytics platforms (e.g., GCP, Azure, AWS) and big data technologies (e.g., Hadoop, Spark).
• Strong technical skills in data mining and transformation, with the ability to work confidently across structured and unstructured data and varied data models. Familiarity with Agile methodologies and collaborative delivery in cross-functional teams.
• Experience with data visualisation tools such as Qlik Sense is a strong advantage. High technical aptitude, intellectual curiosity, strong communication and interpersonal skills, and a clear sense of ownership and accountability
• Strong communication skills, with the ability to engage effectively with both operational teams and senior stakeholders. Support innovative initiatives and help shape the data science strategy. Champion the use of data science to drive fact-based decision-making and behaviours across the function.
• Experience in a large corporate or institutional acquiring environment. Experience supporting the build or scaling of a new fraud operations function or product.
• Experience in financial crime, fraud is beneficial but not essential. Knowledge of banking use cases and how they translate into data science solutions is a plus. Knowledge of scheme monitoring programmes (e.g. Visa Fraud Monitoring Program, Mastercard Excessive Fraud Merchant thresholds).
• Sound knowledge of the Risk Management Framework (expert not required), preferably through previous role.

You’ll achieve more at HSBC 

www.hsbc.com/careers

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”

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