Quantitative Developer

Validus Risk Management
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Quantitative Developer

Quantitative developer

Senior Quantitative Developer

Senior Quantitative Developer

Senior Quantitative Developer

Quantitative Analyst

We are looking for a Quantitative Developer to join our Quantitative Development team. This team is responsible for building and maintaining the firm’s proprietary quantitative risk engine, which underpins our market risk analytics. As part of the wider quantitative group—alongside Quant Research and Quant Strategies —you will play a key role in integrating advanced financial models into our client-facing application Horizon, ensuring scalability, reliability, and performance.


This role sits at the intersection of software engineering and quantitative finance, offering the opportunity to work with multiple teams and directly impact how our models are deployed and used by clients.


Key Responsibilities

  • Design, develop, and maintain components of the in-house quantitative library and risk engine.
  • Collaborate with Quant Research and Quant Strategy teams to implement pricing and risk models for multiple asset classes.
  • Work with Technology and Product teams to integrate quant systems into internal platforms and external client applications.
  • Optimize code and infrastructure for performance, scalability, and stability in production environments.
  • Contribute to the evolution of the firm’s quantitative technology stack, including testing frameworks, CI/CD processes, and coding standards.
  • Support market data integration with market data vendors to ensure accurate pricing and risk calculations.
  • Document system design, development practices, and integration processes for both internal stakeholders and external clients.

Qualifications

  • Minimum 2 years of experience in quantitative development, financial engineering, or risk technology.
  • MSc degree in STEM field.
  • Strong programming skills in Python, including experience with numerical libraries and production-quality code.
  • Experience with cloud platforms (AWS preferred) for deploying and scaling applications.
  • Understanding of FX and Interest Rate trade modelling, pricing, and risk management.
  • Familiarity with market data vendors and OTC market data conventions.
  • Strong grasp of software engineering best practices, including testing, version control, and CI/CD.
  • Ability to work collaboratively across quant, tech, and product teams, while managing multiple development projects.
  • Excellent communication skills and the ability to translate technical work into actionable outputs for both technical and non-technical stakeholders.

Preferred Qualifications

  • Experience with Rust or C++ for performance-critical quantitative development.
  • Exposure to additional asset classes or risk analytics beyond FX and rates.
  • Familiarity with financial risk concepts such as sensitivities, scenario analysis, and stress testing.

Validus Risk Management is an independent technology-enabled advisory firm specialising in the management of FX, interest rate and other market risks. We work with institutional investors, fund managers and portfolio companies to design and implement strategies to measure, manage and monitor financial market risk, using a market-tested combination of specialist consulting services, trade execution and innovative risk technology.


Working at Validus can offer an exciting opportunity for both personal development and professional growth. Share in our mission to become the largest and most respected specialist provider of financial market risk services in the world. Notable benefits include a competitive remuneration package (salary + bonus), pension contributions, regular social events, train ticket loans and financial support towards professional qualifications.


Validus is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.


#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.

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.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.