Lead Data Scientist

Overview

Lead Raylo’s data science function to design, build, and deploy production-grade ML models that drive credit risk, churn, and recommendations. You will shape the data science strategy, mentor the team, and collaborate cross-functionally to unlock growth as the company scales internationally. Expect to redefine ML workflows with agentic architecture and cutting-edge models, delivering measurable commercial impact. This role combines hands-on technical leadership with strategic vision in a fast-growing, AI-driven business.

Pay / Benefits

• Fast-track career progression
• Exclusive Raylo device lease
• Private medical insurance
• Stock options for all employees
• L&D budget for skills
• Hybrid working model

Responsibilities

• Own end-to-end design, build and deployment of production-grade ML models (credit risk, churn prediction, recommendations)
• Partner with product, engineering, risk, operations, and commercial teams to identify high-impact ML opportunities and move them from ideation to action
• Redesign data science processes around agentic architecture and latest LLM models
• Shape and grow Raylo’s data science function to scale over the next five years
• Define and lead the data science roadmap and how AI transforms the data practice
• Contribute to decision-making and impact by translating complex modelling into clear business value

Key requirements

• 7+ years in data science and predictive modelling
• Degree with 2:1 or higher from a top university, preferably STEM or quantitative
• Production deployment of ML models and practical hands-on technical leadership
• Experience coaching and growing a data science function while delivering IC-level projects
• Strong stakeholder management and ability to drive data-driven decision making across diverse teams
• Ownership mindset and proactive delivery in a fast-paced, cross-functional environment

• stakeholder management
• cross-functional collaboration
• strong communication
• state-of-the-art ML models
• production-grade ML deployment
• credit risk modelling

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