Senior Data and ML Engineer

Overview

maintain the data model, data catalogue, data lineage and manage data quality for Fraud. The team oversees and supports the use of the cloud-based fraud decision platform, open-source technologies and agile methodologies. The Data Scientist is responsible for providing Data Science expertise in data, analytics, and ML model development. This includes the development and support of ML algorithms and improving analytics and ML pipelines, ensuring that everything is quality checked, controlled, and governed efficiently and effectively.

They may also provide subject‑matter expertise to the rest of the wider data and analytics teams.

• Assist in the design, implementation of ML algorithms and deploy them in the Feedzai and SASEFM production environments.
• Support, develop and monitor all steps in the end‑to‑end ML lifecycle, including provision of infrastructure and resource management, integration, testing, releasing, deployment and monitoring.
• Ensure and improve the performance of the ML algorithms at scale.
• Produce and manage code repository, artefacts, documentation, user manual and backup.
• Design and develop software to automate the building, testing and deployment of ML algorithms and analytics activities.
• Collaborate with data wranglers, data scientists, data engineers, model governors, and data quality and governance managers to enhance the Graph
  
  DB development and implementation processes, specifically leveraging Tiger Graph, to improve fraud detection and financial crime analytics.

• Significant hands‑on experience in data analysis and development machine learning solutions.
• Proficient in Python, SQL and key libraries such as Scikit‑learn and PySpark.
• Strong track record of designing, building and deploying classification models in a production environment.
• Practical understanding of deep learning techniques.
• Experienced leading end‑to‑end machine learning projects – from problem definition and engineering to development and performance monitoring.
• Experience in cloud‑native solutions, such as data lakes and enterprise big data and analytics platforms, in particular using GCP.
• This hybrid working role can be based in either Birmingham or Sheffield.

As an HSBC employee in the UK, you will have access to tailored professional development opportunities and a competitive pay and benefits package. This includes private healthcare for all UK‑based employees, enhanced maternity and adoption pay and support when you return to work, and a contributory pension scheme with a generous employer contribution.

Opening up a world of opportunity. Being open to different points of view is important for our business and the communities we serve. At HSBC, we are dedicated to creating diverse and inclusive workplaces – no matter their gender, ethnicity, disability, religion, sexual orientation, or age. We are committed to removing barriers and ensuring careers at HSBC are inclusive and accessible for everyone to be at their best.

We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long‑term conditions or neurodivergent candidates who meet the minimum criteria for the role.

If you're looking for a career where you can make a real impression, join HSBC and discover how valued you'll be. 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. Fraud Strategic Analytics specialises in providing state‑of‑art analytics and solutions to the UK Fraud team. The function supports Fraud, Scams and Mule Detection, Prevention and messaging across Payments and Cards. The team builds, automates and maintains the core data and ML (Machine Learning) products for Strategic Fraud Analytics.

The data products are the basis for machine learning, AI (Artificial Intelligence) and visualisation services. The team also build and