Lead Data Scientist - Credit Risk

Experian
London
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Job Description

Our Experian Software Solution's Data Science Team is responsible in supporting analytic and generative AI products for decisioning, analytics, and Fraud and ID globally.

As a Lead Data Scientist, you will use your coding expertise in Python and interest in Gen AI to become a specialist for Experian products.

Main Responsibilities:

  • Collaborate with Engineering and data science teams in the design and implementation of Machine Learning, Dashboarding, Ad Hoc Analysis and AI application on a true big data platform.
  • Partner with Leaders, Sales Engineers, Account Executives, and Product Managers to bring new innovated solutions to market that provide impact to Experian's broad customer base.
  • Stay informed about regulatory changes and technological advancements to ensure the technology stack meets all compliance requirements.
  • Research and integrate new data assets from different resources into Experian's ML and AI platform. Assess analytic tools developed internally and externally.
  • Gather feedback from internal and external customers to guide new product development, feature prioritisation, and product evolution of tools and capabilities supported by the Ascend Platform.


Qualifications

  • Data science background with development expertise in Python
  • Experience developing models in credit risk or credit decisioning
  • Experience building of analytical tools in a regulatory environment
  • A track record for managing complex analytical projects
  • The ability to present to all levels of management within Experian and to our clients



Additional Information

Benefits package includes:

  • Hybrid working
  • Great compensation package and discretionary bonus plan
  • Core benefits include pension, bupa healthcare, sharesave scheme and more
  • 25 days annual leave with 8 bank holidays and 3 volunteering days. You can purchase additional annual leave.

Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, engagement, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; Great Place To Work™ in 24 countries, FORTUNE Best Companies to work and Glassdoor Best Places to Work (globally 4.4 Stars) to name a few. Check out Experian Life on social or our Careers Site to understand why.

Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

Grade:C / EB7

#LI-DS1 #LI-Hybrid

Experian Careers - Creating a better tomorrow together

Find out what its like to work for Experian by clicking here

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.