Stress Testing Modelling Quantitative Analyst

NatWest Group
London
4 days ago
Create job alert
Overview

Join us as a Stress Testing Modelling Quantitative Analyst



  • Take on a pivotal role in our Finance function, in which you’ll be modelling credit projections for stress testing, capital planning and strategy setting purposes
  • You’ll enjoy extensive visibility at a senior level within the bank and with executives and regulators
  • It’s an opportunity to further your career by working with senior stakeholders and enhance your technical skills and knowledge of the business in a fast-paced environment

What you’ll do

We’ll look to you to develop credit risk scenario projections models for the specific Structured Finance / Securitisation business area, liaise with Model Validation to seek approval, document methodology and processes, ensuring a high standard of accuracy and adherence to controls in line with given regulatory timeline. You’ll be providing comprehensive supporting analysis and MI to explain scenario projections results in the context of the given stress scenario, inputs/outputs, models / methodology and assumptions, while engaging with stakeholders and experts from the business areas, Risk, Audit, and other functions to provide business insights and analysis in a timely manner.


Other key aspects of your role will involve:

  • Developing the quantitative methodologies needed to project impairments and credit risk weighted assets, implementing the approved models or analytics solutions
  • Developing adjustments to modelled outputs to address model weaknesses and limitations or apply management judgement
  • Preparing detailed, high quality presentation materials for review and challenge purposes, and presenting modelling results to stakeholders
  • Producing ad hoc insights or analysis, as needed by the business or regulators, to help interpret and use stress testing results and scenario projections
  • Providing accurate evidence and documentation to help complete controls for the credit risk scenario projections process on time

The skills you’ll need

To join us in this role, you’ll need extensive knowledge in securitisation and of credit or financial risk management and measurement, with strong technical and communication skills. We’ll also look for you to have significant experience of credit risk analysis or modelling in a retail or wholesale banking environment, and a thorough understanding of stress testing requirements and the process, models, methodology and controls for generating credit risk scenario projections.


You should also hold a degree in a quantitative discipline such as Mathematics, Statistics, Econometrics or Economics preferably to PhD or post graduate level or with additional professional qualifications, such as a CFA or FRM.

As well as this, you’ll demonstrate:



  • Strong data handling and analysis skills, including programming languages such as Python, and/or C++
  • Extensive knowledge in optimisation algorithms, Machine Learning techniques and regressions
  • Extensive knowledge of Structured Credit and Stress Testing
  • An excellent understanding of the relevant capital requirements regulations, for example Basel II or III, and financial accounting standards such as IFRS 9
  • An organised approach with the ability to manage projects and work effectively to deadlines
  • Excellent attention to detail

Hours

35


Job Posting Closing Date

30/01/2026


Ways of Working

Hybrid


#J-18808-Ljbffr

Related Jobs

View all jobs

Stress Testing Modelling Quantitative Analyst

Senior Stress-Testing Credit Risk Quantitative Modeller

Quantitative Researcher

Senior Quantitative Analyst, Model Data Team, Model Solutions

Data Scientist/Modeller

Risk Manager - Quantitative

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.

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

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.

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.