IFRS9 Credit Risk Modelling Manager

InterQuest Group
Southampton
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Analyst, Model Data Team, Model Solutions

Quantitative Risk Manager, Decision Analytics & Insights

Global Finance Data Architect & Reporting Leader

Insurance Data Governance- Senior Manager

Data Quality Analyst - One Delaware Drive, Milton Keynes, Buckinghamshire

Credit Risk/IFRS9 Modelling Managers | Various Locations |Remote up to £85,000

NO TRANSFER OF SPONSORSHIP AVAILABLE


Exciting opportunities to work with a dynamic retail and commercial banking organisation to help them continue to build their IFRS9 compliant models. This will be on a 2 days per week basis in multiple locations.


Responsibilities

Lead a supportive and collaborative team of modellers and data scientists to create impactful risk and macroeconomic models that help predict the Bank’s loan loss provisions. You’ll guide the development, validation, and ongoing monitoring of IFRS 9 provisioning models, ensuring they provide essential insights to stakeholders across the business while maintaining the highest standards of quality.


  • Lead the design, development, and implementation of credit risk models, ensuring compliance with regulatory requirements and the Bank's standards.
  • Ensure the ongoing relevance and robustness of existing Retail and Business IFRS 9 loan loss provision models.
  • Present model outputs and key insights to stakeholders across the business.
  • Prepare detailed model documentation and share thoughtful recommendations with governance committees.
  • Provide valuable business enablement support and model usage expertise across the Bank.
  • Monitor and address emerging model risks, sharing key findings within the governance framework.
  • Mentor and guide your team, fostering a culture of continuous learning and development.


What We’re Looking For:

  • Significant experience in leading model development for Retail or Business credit portfolios.
  • Strong technical experience with statistical analysis tools such as SAS, Python, or R, and a solid understanding of model application in banking.
  • Excellent communication skills, both written and verbal, with a genuine ability to collaborate and engage with stakeholders.
  • Strong decision-making and problem-solving abilities, with a focus on achieving results.
  • A natural leader who enjoys mentoring and supporting the growth of team members.
  • A collaborative mindset and a commitment to supporting diversity and inclusion within the team.


Our client is passionate about creating an inclusive, supportive, and flexible environment where every individual can thrive. If you’re ready to make a meaningful impact and take the next step in your career, we’d love to hear from you!

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

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Jobs

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

🍪 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.