Quantitative Risk Analyst

Mason Blake
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Analyst

Snr Technical Apps Specialist - Quantitative Risk Management

Snr Technical Apps Specialist - Quantitative Risk Management

Senior Data Strategy Consultant, Marketing Solutions

Genomic Data Scientist in Rare Disease (we have office locations in Cambridge, Leeds & London)

Quantitative Analyst

An exciting opportunity to join a prestigious asset manager in the Fixed Income investment risk team. As part of a small team, this role will provide day-to-day risk management support for the Portfolio Managers, covering fixed income strategies. Additionally, this role will play an active role in various quantitative projects.

Key Responsibilities:

  1. Daily risk management of several fixed income portfolios to ensure that Portfolio Managers understand the different risks in the market
  2. Participate in monthly risk management meetings with Portfolio Managers
  3. Process and understand risk predictions made by risk models
  4. Implement practical machine learning and AI applications covering risk management, portfolio construction and market insights
  5. Conduct analysis of portfolio exposure, performance and key market drivers

Candidate Profile:

  1. Ideally 2-4 years work experience in a quantitative role
  2. Strong understanding of machine learning techniques
  3. Strong interpersonal skills with ability to effectively communicate and influence senior stakeholders
  4. Excellent programming skills (Python, R and/or SQL)
  5. Enthusiasm to research and explore new techniques and drive implementation

Note, this is a highly competitive position. We receive a high volume of applications and are unable to respond to each CV.


#J-18808-Ljbffr

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.

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Jobs

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.

Careers

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.

🍪 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.