Lead Analyst - Model Validation

About this role

Hello, we’re Starling. We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way. We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,500 people across our London, Southampton, Cardiff and Manchester offices.

We are looking for a talented and passionate Lead Analyst - Model Validation to join our Model Risk Oversight Team. AI is a strategic priority at Starling Bank, designed to bring tangible benefits to our customers and significantly improve operational efficiencies. As our use of AI scales across the bank, robust model risk management and strong oversight are essential. The team focuses on validating and overseeing the risks associated with all models across Starling, with an increasing focus on scrutinising advanced AI and ML techniques.

If you have model validation / model build experience across a variety of models and are eager to be part of the team that plays a vital role in model risk management, we want to hear from you! As a Lead Analyst - Model Validation, you will be responsible for: Leading the validation and technical review of Starling's rapidly expanding portfolio of AI/ML models to ensure safe deployment. You will also validate traditional models such as IFRS 9, Forecasting, and Operational Risk.

Producing validation reports whilst engaging with the model developers and model owners to ensure the validation findings are understood and can be addressed within the agreed timelines. Producing committee papers and presenting at committees. Presenting high-level validation findings to Senior leadership. Engaging and forming close relationships with the key stakeholders to enable good model risk management practices throughout the model lifecycle.

This role will close at 12pm on Thursday 29th October. Requirements Graduate level education preferable, ideally in a numerical degree (or an equivalent work experience); Proven, hands-on experience in AI/ML model validation or direct AI model build experience is required. You must possess a deep understanding of advanced algorithms to critically challenge model developers, alongside experience with traditional credit risk or forecasting models.