Quant Developer (Python/C++) - Model Implementation - London- Global Hedge Fund

Oxford Knight
London
5 months ago
Applications closed

Related Jobs

View all jobs

Senior KDB+ Developer - Quant Hedge Fund

Investments Data Analyst

Quant Trading - Data Scientist

Contract Python Software Engineer - Trading

Quants Analyst (AVP Level)

Investment Quant

Total Comp:>£300k

A leading systematic hedge fund, investing across a variety of financial markets in multiple locations, my client is seeking a creative problem-solver to be the next Quant Developer in their growing Model Implementation team.

This team is comprised of technical and hands-on builders, each wearing multiple hats, and in this role you'll be expected to do the same. Working very closely with Researchers and PMs on the team, your primary responsibility will be the distributed real-time trading system for computing signals, and targeting positions for various strategies. You'll also own the design and production implementation of new strategies, lead efforts to identify and tackle platform bottlenecks, as well as adding expanding the platform capabilities to new asset classes.

The successful Quant Developer will have a strong work ethic, fantastic multi-tasking ability and a good sense of accountability.

Requirements

  • Minimum 5+ years of Quant Developer experience (or similar position)
  • Strong coding experience in Python and C++, with outstanding debugging and analytical skills
  • Experience with Python data science stack, e.g. Pandas/Numpy/Scikit-learn
  • Keen proponent of writing automated tests
  • BS/MS/PhD in Computer Science (or equivalent)


Benefits

  • Competitive base salaries and performance-based bonuses
  • Very collaborative culture, ideas are implemented
  • Work with passionate, forward-thinking, incredibly smart people



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.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.