InterQuest Group (UK) Limited | Model Risk Senior Analyst

InterQuest Group (UK) Limited
Newcastle upon Tyne
1 year ago
Applications closed

Related Jobs

View all jobs

Reader in Artificial Intelligence

Research Assistant/Associate in Data Science and Computational Neuroscience

Data Scientist Intern (PhD level)

Junior Data Scientist Python SQL - HealthTech

Senior Data Scientist

Senior Geospatial Data Scientist

Are you ready to make a difference in the banking industry by ensuring robust and reliable models that drive critical decisions? Our client is looking for a passionate and skilledModel Risk Managerto join their team, where innovation meets responsibility.

Your Key Role:

  • Enhance our Model Risk Management Frameworkto uphold regulatory standards and mitigate risks effectively.
  • Independently validate and review models, includingstochastic modelsanddeterministic quantitative methods, that underpin critical banking decisions.
  • Ensure compliance with regulatory frameworks such asICAAP,ILAAP, and IRB requirements.
  • Collaborate with teams across the bank to identify, assess, and mitigate model risks.
  • Present your insights and findings to theModel Oversight Committee (MOC)and senior management.
  • Stay ahead of regulatory developments and integrateindustry best practicesinto our banking operations.

What You'll Bring:

  • Experience inIRB model validationand knowledge of key regulations likeCRR, EBA, and PRA.
  • A strong analytical background, with expertise inSAS, SQL, R, or Pythonto support model assessments.
  • Educational credentials instatistics, mathematics, data science, or engineering.
  • Exceptional communication skills to engage with stakeholders across all levels of the bank.
  • A proactive, detail-oriented mindset with a collaborative approach to problem-solving.

InterQuest Group is acting as an employment agency for this vacancy. InterQuest Group is an equal opportunities employer and we welcome applications from all suitably qualified persons regardless of age, disability, gender, religion/belief, race, marriage, civil partnership, pregnancy, maternity, sex or sexual orientation. Please make us aware if you require any reasonable adjustments throughout the recruitment process.


JBRP1_UKTJ

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.