Model Validation Data Scientist

Join us as a Model Risk Data Scientist

• If you’re a keen problem solver and you’re passionate about data, machine learning and statistics, we think you’ll enjoy a real sense of purpose in this role
• You’ll be harnessing your mathematical prowess to validate our data-driven models and explore ways to automate and enhance their validation processes
• It’s your chance to be part of a collaborative community of data enthusiasts, discover new algorithms, tools and data ecosystems, and develop specialist knowledge that will see you become an expert in your field – and pave the way for further career success

What you'll do

In today’s rapidly changing world, our ability to understand tomorrow and make better decisions today is key. And while we can’t see into the future, we can use data-driven models to guide our business practices, identify uncertainties and uncover valuable insights.

But reliance on models invariably presents its own risks. And as our Model Risk Data Scientist, it’ll be your job to review and independently validate our data-driven models to determine how they’re being used, their accuracy and dependencies, and the level of model risk they pose.

Day to day, you’ll be:

• Using data and analytics to review the data-driven models that we use across our bank and
• Developing the validation framework for Gen AI models across the bank
• Sharing your findings with your stakeholders and building consensus on how model risks can be mitigated
• Exploring ways to automate and enhance our model validation activities
• Collaborating with model developers to increase the value generated by data-driven modelling
• Developing our analytics codebase by adding new functionality, fixing issues and testing code

The skills you'll need

With practical experience of building and validating data-driven models, you’ll bring the creativity, determination and perseverance that comes with tackling ideas that are hard to understand and problems that are hard to solve.

You’ll also bring great working habits too, like being organised, thorough and painstaking in your work, great at working under pressure, and equally content working on your own or together as a team.

And you’ll bring this all together with:

• A good understanding of the mathematical methods, concepts and assumptions that underpin machine learning, statistical modelling and artificial intelligence
• Python programming experience and commonly used libraries in data science
• An appreciation of the practicalities that working with real-world datasets presents, and the operational challenge of deploying data-driven models
• Your ability to uncover and extract meaningful insights from technical results and relay these in a way that’s easy to understand

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