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Credit risk data scientist

Slough
Harnham Search & Selection
Data scientist
Posted: 8 March
Offer description

Do you want to rebuild commercial credit models used by lenders across the UK?

Have you worked hands-on with SME or corporate lending data end to end?

Are you looking for a stable, high-impact analytics role with real ownership?


Company overview

This organisation is a leading UK credit data provider operating at the heart of the lending ecosystem. They work with banks, fintechs, and commercial lenders to improve credit decision-making through data, analytics, and risk products. The environment is collaborative, stable, and low-turnover, with long-term investment in analytics rather than hype-driven AI.


The role

This is a hybrid Data Scientist / Model Developer position within the commercial lending product team. You will rebuild and enhance core credit products used by lenders, owning models end to end and working with rich commercial datasets.


Key responsibilities

• Build and rebuild commercial credit scorecards and decision models

• Develop affordability, segmentation, and forecasting models

• Own models end to end from data exploration to deployment

• Work with commercial datasets such as company registrations and filings

• Contribute to portfolio analytics and ad-hoc analytical projects

• Support the evolution of legacy products into modern solutions


Key details

• Salary: up to £75k base + bonus and standard benefits

• Location: London preferred; Leeds or Nottingham considered

• Working model: Hybrid, 3 days onsite (Tues–Thurs)

• Tech stack: Python, SQL

• Visa sponsorship: Not available


Requirements

• 3+ years’ experience in data science or credit risk modelling

• Proven experience with commercial or business lending data (SME/corporate)

• Strong Python modelling capability; SQL for data access

• Background in credit scorecards, affordability, segmentation, forecasting, or NPV modelling

• STEM degree

• Hands-on, delivery-focused mindset


Interested? Please apply below.

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