Senior Credit Risk Analyst (Lead) - Consumer Lending / Loans

Nottingham
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Credit Data Scientist

Senior Data Scientist

Head of Data Science -Telematics

Head of Data Science -Telematics

Portfolio Revenue & Debt Data Scientist

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

This rapidly expanding financial services company are seeking a Senior Credit Risk Analyst to join their Consumer Lending function. Working with the Commercial Director you develop credit risk analytics solutions to enhance Credit Scoring & Lending decisioning to optimise and grow their loan portfolio.

Client Details

Rapidly expanding financial services company

Opportunity to develop and enhance credit risk modelling & analytics strategy

Description

This rapidly expanding financial services company are seeking a Senior Credit Risk Analyst to join their Consumer Lending function. Working with the Commercial Director you develop credit risk analytics solutions to enhance Credit Scoring & Lending decisioning to optimise and grow their loan portfolio.

Key Responsibilities:

Developing and implementing advanced statistical models and preferably machine learning algorithms to predict credit risk, optimise credit scoring, and enhance our decision-making/underwriting processes.
Develop and maintain predictive models to assess credit risk and forecast customer behaviour.
Analyse large datasets to identify trends, patterns, and insights that inform business decisions.
Perform data cleaning to ensure high-quality data for analysis,
Conduct A/B testing and other experiments to evaluate the impact of credit strategies and policies.
Develop credit risk models, such as probability of default (PD) using various modelling techniques.
Working independently and presenting findings and recommendations to stakeholders in a clear and concise manner.

Key Skills / Experience:

Experience in the Financial Services Industry (Essential)
Experience working with large data sets, Excel and SQL proficiency (Essential)
Degree in relevant subject (Data Science, Statistics, Computer Science, Economics or similar degree) (Essential)
Strong proficiency in programming languages such as Python, R (Preferable)
Experience using Salesforce and data visualisation tools (Preferable)
Strong presentation skills, including the ability to translate complex data into understandable insight
A great attention to detail and be process-oriented to review, suggest and implement improvements where appropriate.
Able to work in a fast paced, changing environment.

Profile

Experience in the Financial Services Industry (Essential)
Experience working with large data sets, Excel and SQL proficiency (Essential)
Degree in relevant subject (Data Science, Statistics, Computer Science, Economics or similar degree) (Essential)
Strong proficiency in programming languages such as Python, R (Preferable)
Experience using Salesforce and data visualisation tools (Preferable)
Strong presentation skills, including the ability to translate complex data into understandable insight
A great attention to detail and be process-oriented to review, suggest and implement improvements where appropriate.
Able to work in a fast paced, changing environment.Job Offer

Opportunity to join a rapidly expanding financial services company

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.

Jobs Careers

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

Jobs

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Education Jobs

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.