Head of Data Science

Overview

As Head of Data Science at Raylo, you will define and lead a production-grade ML and AI function that drives growth, risk management, and personalized recommendations. You’ll shape the data science roadmap, deliver end-to-end models from idea to deployment, and partner across product, engineering, risk, and commercial teams. You’ll accelerate Raylo’s AI-powered decision making while scaling the data function to support international growth. This is a rare opportunity to build best-in-class models and define the strategic direction of a category-defining business.

Pay / Benefits

• Fast-track your career
• Device lease for employees
• Private Medical Insurance
• Stock options for all employees
• Hybrid working model
• 33 days off

Responsibilities

• Define and lead Raylo’s data science function and deploy cutting-edge AI/ML solutions
• Own end-to-end design, build and deployment of production-grade models (credit risk, churn, recommendations)
• Collaborate with product, engineering, risk, operations and commercial teams to identify high-impact ML opportunities
• Translate complex modelling approaches into clear business value and commercial benefits
• Start as a hands-on technical leader and shape the data science roadmap
• Drive transformation of data practices and build the function to scale over the next five years
• Influence key business outcomes such as risk management, retention, and international growth

Key requirements

• 7+ years of experience in data science and predictive modelling
• 2:1 or higher from a top university, preferably in a STEM/quantitative discipline
• Experience building and deploying state-of-the-art ML models in production
• Track record of coaching and growing a data science function while delivering IC projects
• Strong stakeholder management and ability to champion data-driven decision making
• Comfort working in a fast-paced, cross-functional environment and owning end-to-end work

• stakeholder management
• clear technical communication
• ownership and accountability
• production-grade ML models
• predictive modelling
• AI/ML development with modern frameworks

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