Senior Credit Risk Analyst - Consumer Lending / Loans

Birmingham
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Risk Analyst

Quantitative Risk Manager, IFRS9, Multiple Locations, Level 4

Portfolio Revenue & Debt Data Scientist

Portfolio Revenue & Debt Data Scientist

Quantitative Researcher- Cross-Asset Relative Value

Senior Data Governance Analyst

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 will develop credit risk analytics / scorecard modelling solutions to enhance Credit Scoring & Lending decisioning to optimise and grow their loan portfolio

Client Details

Rapidly expanding financial services company

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 will develop credit risk analytics / scorecard modelling solutions to enhance Credit Scoring & Lending decisioning to optimise and grow their loan portfolio.

Key Responsibilities:

Developing and implementing advanced statistical / scorecard models to predict credit risk, optimise credit scoring, and enhance 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 (Essential)
Proficiency in Python, R, SQL or other programming languages (Essential)
Proficiency in Excel (Essential)
Strong presentation skills, including the ability to translate complex data into understandable insight (Essential)
A great attention to detail and be process-oriented to review, suggest and implement improvements where appropriate. (Essential)
Able to work in a fast paced, changing environment.(Essential)
Degree in relevant subject (Data Science, Statistics, Computer Science, Economics or similar degree) (Preferable)
Experience using Salesforce and data visualisation tools (Preferable)Profile

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

Opportunity to develop and enhance credit risk modelling & analytics strategy

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 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.