Data Scientist - HSBC

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 an exciting new direction, we offer opportunities, support and rewards that will take you further.

We’re one of the largest banking and financial services organisations in the world, with a network that covers more than 50 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people fulfil their hopes and realise their ambitions.

We’re currently seeking an experienced professional to join our Economic Crime Prevention Team in the role of Data Scientist.

This role puts your data science where it counts, protecting people in the moments they’re most vulnerable to scams and fraud. You’ll develop and deploy real-time Machine Learning models that help stop suspicious outbound payments while keeping legitimate customer activity smooth. It’s a chance to work on problems with real-world consequences, at scale, with end-to-end ownership from modelling to production and monitoring. If you want to build Machine Learning that genuinely improves lives, you’ll feel the difference here.

A move across the business allows you to continue to access tailored professional development opportunities, and our fantastic benefits packages.

In this role you’ll:

• Design, develop, and optimise machine learning models to detect fraud and scams across outbound payment journeys
• Engineer features and labels from high volume event streams and historical datasets to improve model signal and stability
• Evaluate model performance using business relevant metrics (e.g., detection rate, false positives, customer friction, operational impact) and translate these into clear recommendations
• Run controlled experiments to validate improvements safely in production
• Implement Machine Learning algorithms and deploy them into Feedzai production environments
• Support and monitor all steps in the Machine Learning lifecycle: development, integration, testing, release, deployment, ongoing monitoring and governance
• Produce and maintain code repositories, artefacts, documentation, user guides, and backups aligned to governance requirements
• Partner closely with fraud strategy, operations, product, and technology colleagues to ensure models are practical, explainable, and deliver measurable outcomes

To be successful in this role you should meet the following requirements:

• Background or knowledge in applied and theoretical data science is essential
• Ability to code in SQL and Python
• Hands on experience using Scikit, numPy and Pandas
• Classification modelling experience, you’ve designed, built, and evaluated classification models; familiarity with deep learning concepts is a plus