Quant Researcher

Selby Jennings
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Quantitative Researcher

Machine Learning Quantitative Researcher

Senior Data Engineer: Quant & Alternative Data Platform

Senior Data Engineer, Quant & Alt Data | Python/SQL | Relocation

Senior Data Engineer - Quant & Alt Data (Relocation)

Senior Data Engineer: Quant & Alternative Data

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.