Overview

A fast-growing fintech is hiring a Senior Data Scientist on a permanent, full-time basis in London, with at least three days a week in the office (Mondays are a fixed in-office day for the whole team). The salary is £94,589 to £111,502 depending on experience, with stock options of £80,479 to £105,003 on top. Benefits include 35 days holiday, private healthcare with Bupa, up to 12 weeks enhanced parental leave, a £750 annual learning allowance, and a cycle-to-work scheme.

This role exists because the business is still building its predictive modelling capability, not inheriting a finished one. You will own the full modelling lifecycle across credit acquisition, portfolio management, collections, fraud and rewards: from initial hypothesis and feature engineering through to production deployment, monitoring and iteration. Your outputs feed directly into a credit strategist's decision-making, so clarity and usability matter as much as model performance. You will report to the CRO and work shoulder-to-shoulder with the teams making live commercial decisions. If you want to build something that genuinely shapes how a consumer credit business runs, rather than polish models in a central team, this is the role.

Key Responsibilities

• Build and continuously improve acquisition models using bureau, application and alternative data to assess applicant risk, affordability and expected customer value
• Develop behavioural models for portfolio management, helping the credit strategy team improve credit profitability
• Build collections segmentation models that predict member responses to different interventions and focus effort where it has the greatest impact
• Develop and improve fraud detection models, identifying suspicious behaviour earlier and reducing friction for genuine members
• Work with AI engineers to use member data to surface the right reward at the right time
• Own the full modelling lifecycle: data exploration, feature engineering, training, validation, implementation, and ongoing performance monitoring
• Get models into production yourself, not hand them off to engineers
• Identify additional internal signals, bureau attributes and external data sources that could materially improve prediction
• Build robust model monitoring and validation so the team understands calibration, discrimination and drift after deployment
• Explain model outputs and methodology clearly to the CRO and committee members without relying on technical language
• Help establish the modelling standards, tooling and experimentation practices that will define how data science works at the business