Quantitative Developer, Equities & Futures

Multi-Strat Hedge Fund
London
2 months ago
Applications closed

Related Jobs

View all jobs

Regional Finance Director

Finance Director

Senior Quantitative Analyst

Policy Analyst

User Experience Designer – Digital

Business Analyst

Job Title: Quantitative Developer (C++)


Location:London, UK

Department:Equities and Futures Trading Pod

Position Type:Full-time

Reports to:Portfolio Manager


About the Company

Join a leading global hedge fund with a strong track record of delivering superior returns through sophisticated trading strategies and cutting-edge technology. Our firm operates across various asset classes, including equities, futures, fixed income, and more. We foster a collaborative environment where innovative ideas are encouraged, and employees are empowered to excel.


Role Overview

We are seeking a highly skilled Quantitative Developer with expertise in C++ to join our Equities and Futures Trading Pod in London. This role is integral to the development and enhancement of our trading algorithms, quantitative models, and high-performance trading systems. The ideal candidate will have a strong background in quantitative development, a deep understanding of financial markets, and the ability to work closely with traders, quantitative researchers, and other developers.


Key Responsibilities

  • Algorithm Development:Design, develop, and optimize trading algorithms for equities and futures markets using C++.
  • Quantitative Model Implementation:Collaborate with quantitative researchers to translate mathematical models into robust, efficient, and scalable code.
  • System Performance Optimization:Ensure high performance and low latency of trading systems through rigorous optimization and testing of C++ code.
  • Backtesting and Simulation:Develop tools and frameworks for backtesting trading strategies and running simulations to validate model performance.
  • Data Analysis:Analyze large datasets to identify patterns, trends, and opportunities that can inform trading strategies.
  • Collaboration:Work closely with traders, quants, and other developers to integrate new features and enhancements into the trading platform.
  • Code Review & Maintenance:Participate in code reviews, maintain high code quality standards, and contribute to the continuous improvement of development practices.
  • Risk Management:Develop and integrate risk management tools to monitor and mitigate potential risks in trading strategies.


Qualifications

  • Educational Background:Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline.


Programming Skills:

  • Expertise in C++:Strong proficiency in C++ (11/14/17) with a focus on performance optimization, memory management, and multithreading.
  • Additional Languages:Proficiency in Python, R, or other scripting languages is a plus.


Financial Knowledge:

  • Market Expertise:In-depth knowledge of equities and futures markets, with a strong understanding of market microstructure and trading strategies.
  • Quantitative Skills:Familiarity with mathematical modeling, statistical analysis, and machine learning techniques.


Experience:

  • Industry Experience:3+ years of experience as a Quantitative Developer or similar role within a hedge fund, proprietary trading firm, or investment bank.
  • Algorithmic Trading:Proven track record of developing and deploying successful trading algorithms in live markets.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Careers

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Careers

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

🍪 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.