Lead Credit Risk Analyst - SME Lending

Harnham
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Head of Data Science -Telematics

Head of Data Science -Telematics

Sr. Data Scientist

Portfolio Revenue & Debt Data Scientist

Data Scientist - Credit Risk & AI Innovation

Senior Machine Learning & AI Engineer

LEAD CREDIT RISK ANALYST – SME LENDING

£85,000

LONDON

This role offers the chance to work on a range of project areas including lending strategy, pricing modelling and wider portfolio analytics to grow out the SME lending book of a well-known and successful FinTech.

THE COMPANY

This is an exciting opportunity with a leading UK FinTech that focus on harnessing data to enhance their lending decisions. This business have been growing in recent years and are now in an excellent position where they are targeting further success.

THE ROLE

  • Developing lending strategies across the customer lifecycle to enhance profitability and decisioning
  • Developing and implementing models, including but not limited to Scorecards and NPV models, in order to enhance lending
  • Analysing trends in lending portfolios to drive insight and enhance business performance
  • Leading the incorporation of new data sources to enhance and improve decisioning, collaborating with teams including Underwriting, Data Science and Marketing

YOUR SKILLS AND EXPERIENCE

  • Previous experience in and knowledge of SQL is essential, Python desirable
  • Experience in developing lending strategies in a consumer lending environment is essential
  • Understanding of and exposure to credit risk models, such as scorecards
  • Experience with SME or wider business lending is essential

SALARY AND BENEFITS

  • Base salary of up to £85,000
  • Discretionary Bonus
  • Company pension scheme
  • Company equity
  • 25 days holiday

HOW TO APPLY

Please register your interest by sending your CV to Rosie Walsh through the ‘Apply’ link

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.

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.

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.

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.