Role Purpose

The role of Machine Learning in front office analytics has evolved significantly in recent years, with the development of in-house AI Platforms, and increased experimentation with ML for pricing, heading, risk, and also research. As the prevalence of machine learning and it use across the MSS business matures

Ultimately the role holder will be responsible for enabling front office teams to more easily experiment with machine learning and will work closely with technology teams to oversee the delivery of robust production solutions.

The role holder will ensure the fair treatment of our customers is at the heart of everything we do, both personally and as an organisation. This will be achieved by consistently displaying the behaviours that form part of the HSBC Values and culture and adhering to HSBC risk policies and procedures, including notification and escalation of any concerns and taking required action in relation to points raised by audit and/or external regulators.

In this role, you will:

• Support quantitative researchers and data scientists in taking offline models and turning them into scalable, robust production systems.
• Apply software engineering best practices to data processing and machine learning projects, including CI/CD, automation, design for test, etc.
• Work with MSS Technology teams to design and build data pipelines, engineering infrastructure, and model training/serving pipelines to support research projects across MSS.
• Proactively identify, evaluate, and implement new technologies and practices to improve performance and maintainability of research and production codebases.
• Train and mentor quant dev and tech communities in ML Ops best practices.
• Develop and deploy ML Ops tooling.

Requirements

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

• Experience in developing software engineering for financial services.
• Experience working with building machine learning pipelines for/with front office data science or quant teams at a Tier 1 institution.
• Bachelors or Masters Degree in Computer Science or a related engineering discipline.
• Experience in translating business needs into technical requirements and building end-to-end systems.
• Experience building and deploying machine learning applications in production.
• Solid grasp of both software engineering and machine learning fundamentals.
• Familiarity with data-oriented workflow orchestration frameworks and experiment tracking frameworks (e.g. Airflow, MLFlow, Kubeflow, DVC, etc).
• Exposure to machine learning methodology and best practices.

You’ll achieve more when you join HSBC.