Credit Risk Data Scientist

Harnham
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Credit Risk Data Scientist: Revenue & Debt Analytics

Credit Risk Data Scientist: Revenue & Debt Analytics

Credit Risk Data Scientist: Portfolio & Debt Analytics

Senior Credit Data Scientist

Senior Data Scientist – US Credit Risk ML, Remote M/W F

Senior Data Scientist - IFRS / Credit Risk Modelling

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.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.