Head of Data Science & AI

About the Role

We’re looking for a Head of Data Science & AI. You'll lead [Employer hidden]'s data science function – a group building probabilistic and statistical models that make real-time lending decisions. You'll also be a key part of how [Employer hidden] embeds AI across the wider business: setting the pace yourself, and raising capability beyond your direct team.

As Head of Data Science & AI, you'll shape how a technically sophisticated team works – and influence how the wider business adopts AI. Doing that well requires a deep understanding of the modelling work: where AI genuinely changes the work, where it speeds things up, and where a different approach is better. You'll judge what's actually possible with these tools – and challenge the assumptions when the rationale isn't clear.

[Employer hidden]'s data scientists build probabilistic ML models on credit and commercial data, deployed in production, making real-time lending decisions across lending, product, operations, and strategy. The group has approximately 25 data scientists, split across a central team and smaller groups aligned to specific products or domains. You'll report to one of [Employer hidden]'s co-founders, who is also a data scientist.

Key Responsibilities

• AI enablement and adoption: You'll set the standard for how AI is used across the group – establishing practices that make adoption safe and repeatable, and creating the conditions for a technically sophisticated group to advance together.
• Strategic direction: You'll influence where the group invests its resources – deciding what to model, where AI accelerates the work, and where a lighter approach is more effective. You'll shape commercial and product decisions by making analytical trade-offs legible to senior stakeholders, and work with team leads to plan and prioritise across multiple streams.
• People and team: You'll develop the people around you – raising capability across the group through clear standards, direct coaching, and a genuine investment in how data scientists grow. You'll spot where the gaps are and help close them. You'll also own hiring, shaping how the group assesses and develops talent as it grows.
• Commercial opportunity and coordination: You'll spot commercial opportunities across the business – where modelling or AI can change outcomes – and work with Engineering, Product, and Operations teams to act on them. You'll represent the group in discussions that shape lending, risk, and product decisions, explaining assumptions, highlighting risks, and helping senior stakeholders act on analytical insight.

Who you are

• High AI agency: You actively experiment with AI in your analytical and technical workflows. You use tools like Claude Code, Codex, or similar to build, automate, and accelerate substantive work. You have a clear view on how AI changes what a data science team does and a track record of raising capability across the people around you, not just your own output.
• Transformation track record: You have led a meaningful shift in how an analytical or data science team works. You are comfortable navigating resistance, building adoption across different types of people, and making change persist beyond your direct involvement.
• Technical background: You have a background in probability, statistics, or a related quantitative field, with hands-on experience building and overseeing probabilistic ML models on structured commercial or financial data.
• Production experience: You have managed the full lifecycle of models in production – deploying, monitoring, and retiring them. You are comfortable coordinating chains of model dependencies across different teams.
• Commercial acumen: You understand how modelling supports business decisions and know when to make trade-offs between depth, delivery time, and value.
• Strategic leadership: You have experience setting data science strategy, aligning work with commercial goals, and translating technical modelling for senior stakeholders so they can act on it.
• People and team: You have experience managing a data science team, setting clear standards, and developing people – including having direct conversations about where the gap is and how to close it.

Desirable Skills

• Domain experience: You have worked in credit risk, lending, or customer lifetime value modelling.
• Function scale: You have led a data science team of 20 or more people across multiple teams.
• R&D and forecasting: You have experience shaping a modelling agenda, including probabilistic or long-term forecasting work.
• Industry profile: You have represented a data science team externally – industry events, publications, or advisory roles.

Application Process

We’re open-minded, so definitely include your salary goals with your application.