Quantitative Risk Manager

Arthur
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Strategy Consultant, Marketing Solutions

Snr Technical Apps Specialist - Quantitative Risk Management

Snr Technical Apps Specialist - Quantitative Risk Management

Quantitative Analyst

Quantitative Analyst

Environmental Data Analyst

We’re looking for a talented Quantitative Risk Manager to join a dynamic team. This is a fantastic opportunity for a risk professional with strong analytical expertise to play a key role in model validation, stress testing, within a forward-thinking risk function.Key responsibilities:

  • Lead high-level validation of key models, ensuring they are robust, fit for purpose, and aligned to core business and regulatory requirements.
  • Maintain and monitor Risk Appetite Statements within the broader Enterprise Risk Management framework, ensuring risk exposures remain within approved tolerances.
  • Contribute to the production of the annual Own Risk and Solvency Assessment (ORSA), providing quantitative analysis and clear risk insights to support decision-making.
  • Design and quantify stress and scenario testing across ORSA, capital setting, operational risk, validation, and business plan stress testing to assess resilience under adverse conditions.

If you are looking for a more quantitative role or a step up, this could be the one for you. Reach out to for more info!...

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.

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.