Join us as a Model Risk Data Scientist

• This is an opportunity for a passionate and driven quantitative risk specialist to join an evolving area of risk management
• We’ll look to you to review and validate various pricing, risk & forecasting models across Finance and Treasury functions of NatWest group
• It's an ideal role to gain detailed exposure to the developing world of model risk, as well as to a range of stakeholders and senior executives

What you'll do

As a Model Risk Data Scientist, you will provide oversight of the organisation's data-driven models through data analytics and model reviews. By conducting thorough quantitative analysis, you’ll assess their performance and robustness.

You will be undertaking analysis to make sure that all sources of model risks are adequately highlighted as well as assessing the models’ compliance with regulations, internal policies and standards.

You’ll also be:

• Performing sensitivity analysis to assess the adequacy of modelling or data assumptions, documenting all the analysis in a succinct and clear manner
• Undertaking in-depth assessments of the models’ subcomponents, making sure models are fit for purpose for their designated use
• Preparing checklists for various validation activity to make sure that appropriate controls are established and consistently followed
• Providing expert advice on aspects of risk management, including providing senior executives with relevant MI and reports

The skills you'll need

We’re looking for someone with a quantitative degree and experience of developing, validating and implementing analytical solutions.

You will demonstrate a strong quantitative and statistical expertise with solid foundation in probability theory, statistics, calculus and time series analysis.

You’ll also need:

• Proficiency in data manipulation and analysis using tools like Python
• Awareness of the regulations and requirements surrounding model risk, including SS 1/23
• Experience in writing and proof-reading technical papers

Hours

Job Posting Closing Date: