Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Quant Researcher

Selby Jennings
London
11 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Quant Engineer - Investment banking/ XVA

Machine Learning Quant Engineer - Investment Banking

Data Engineer - Trading Systems - Quant Fund

Data Engineer - Python - Quant Finance

Quantitative Data Engineer

Quantitative Data Engineer

I am working with an established pod at a $15 Bn+ hedge fund inLondonwho are looking for a mid-frequency Quantitative Researcher to work on the research, development and execution of theirfutures strategies.

The PM has been in his seat for 2 years, with the pod running for 5+ years. You would be working on fully systematicalpha strategies within futures, with holding period of intraday up to a week. This can be across all liquid asset classes e.g. FX futures, Rates futures, Commodities futures, Fixed Income futures.

Key Responsibilities:

  • Alpha Strategy Development:Design, test, and implement quantitative alpha strategies focusing on futures markets, using advanced statistical and machine learning techniques.
  • Data Analysis:Leverage large datasets (historical price data, macroeconomic indicators, sentiment data, etc.) to identify patterns, correlations, and predictive signals that can be incorporated into models.
  • Modeling & Backtesting:Develop quantitative models and utilise backtesting frameworks to assess the effectiveness and robustness of strategies under various market conditions.
  • Research & Innovation:Stay up to date with the latest developments in financial markets, quantitative research techniques, and algorithmic trading to continuously innovate and improve alpha generation capabilities.
  • Collaboration:Work closely with the PM to ensure smooth implementation of models and strategies, providing insights and analysis to optimize trading decisions.
  • Performance Evaluation:Continuously monitor and evaluate the performance of live strategies, optimizing parameters and making necessary adjustments to improve performance.

Qualifications:

  • Education:Advanced degree (Master's or PhD) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Finance, or Statistics.
  • Experience:
    • At least 2-6 years of experience in quantitative research, with a focus on alpha strategy development and futures markets.
    • Experience with futures products (e.g., equity index futures, commodity futures, fixed-income futures) and related market structures.
    • Proficiency in statistical and machine learning techniques such as regression analysis, time series modeling, Monte Carlo simulations, and optimization.
    • Strong coding skills in Python and similar programming languages; experience with backtesting platforms (e.g., QuantConnect, Backtrader, etc.) is a plus.
  • Skills:
    • Strong quantitative and analytical skills, with the ability to extract insights from complex datasets.
    • Proficiency in data manipulation, statistical analysis, and visualization tools (e.g., Pandas, NumPy, SciPy, Matplotlib).
    • Strong understanding of financial markets, trading mechanics, and futures contracts.
    • Excellent problem-solving and critical thinking abilities.
    • Effective communication skills, with the ability to present research findings and strategies clearly to non-technical stakeholders.

Q2xhcmEuT0RvaGVydHkuMTE4OTMuZWZpQHNlbGJ5bG9uZG9uLmFwbGl0cmFrLmNvbQ.gif

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.