Data Science Manager

Overview

In this role you will lead the data science function within Teya’s Credit & Analytics team, owning credit risk, pricing and customer value models that support lending products. You’ll steer a team of data scientists, define the modelling roadmap, and manage the full model lifecycle from problem framing to production deployment. You will collaborate with Credit Strategy, Product, Finance and Engineering to ensure models are sound, robust and commercially meaningful across multiple geographies. This is a fast-growing fintech opportunity to shape credit decisions and portfolio performance while building a high-impact data culture.

Pay / Benefits

• flexible working hours
• WellHub health & fitness support
• enhanced maternity/paternity leave
• Cycle-to-Work Scheme
• Health and Life Insurance
• Pension Scheme

Responsibilities

• Lead, mentor and develop a team of data scientists with clear goals and regular feedback
• Own hiring for the data science team and elevate candidate quality
• Translate credit and commercial priorities into a data science roadmap and communicate trade-offs
• Lead development of credit risk models, pricing engines and customer lifetime value frameworks
• Shape the decisioning framework for credit decisions and bring it into production with cross-functional partners
• Own model governance across lifecycle including validation, testing and monitoring for regulatory scrutiny
• Identify new data sources and modelling techniques to maintain competitive edge
• Communicate model outputs and business impact to senior stakeholders and executives

Key requirements

• 7+ years in data science or quantitative modelling with hands-on credit risk modelling experience in SME lending or fintech
• 2+ years of people leadership including hiring and coaching
• Degree in a quantitative field; advanced degree a plus
• Strong applied quantitative skills and understanding of model validation
• Proficiency in Python and SQL with production deployment experience
• Track record of leading models through full lifecycle from problem to deployment and monitoring
• Strong commercial judgement linking modelling to credit performance, pricing and strategy
• Excellent communication and stakeholder management skills; able to influence across technical and non-technical teams
• Familiarity with lending economics is a plus

• leadership and people management
• clear communication with non-technical stakeholders
• commercial judgement
• Python
• SQL
• machine learning techniques

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