Model Validation Data Scientist

Join us as a Model Risk Data Scientist

• If you have experience building and validating AI and machine learning models, this is a fantastic opportunity join our innovative, vibrant team in our Risk function
• You’ll be performing technical reviews and oversight of AI models used in the bank, whilst working with model development teams

What you'll do

This Model Risk Data Scientist role will see you reviewing and independently validating assigned models in accordance with the bank’s policies and model standards. You'll be responsible for designing and developing an evaluation framework for Gen AI and agentic AI models.

You’ll communicate your findings and recommendations to stakeholders and advise on how model risk can be reduced or mitigated.

As well as this, you’ll be developing solutions for automating validation activities while understanding model and data usage, quality and interdependencies across the bank.

Your role will also involve:

• Developing the team's analytics codebase, adding functionality, fixing issues and testing code
• Conducting research on latest LLM evaluation methods based on use case specific challenges
• Contributing to the development of an efficient and scalable evaluation package to be used by the independent validation function
• Reviewing your colleagues’ analysis, code and reports
• Representing the team at model governance forums and other meetings
• Assisting the leadership team with managing the team's tasks and workflow and helping your team with their training and development

The skills you'll need

We’re looking for someone with an excellent grasp of mathematical methods, concepts and assumptions that underpin machine learning, statistical modelling and artificial intelligence.

You’ll also need:

• A proficiency in Python and libraries commonly used for data science, such as Linux WSL and AWS Sagemaker
• Practical experience building and validating Large Language Models
• The ability to extract the essential ideas underlying technical results and explain them in terms of their practical consequences
• The ability to deal with ambiguity and to work autonomously