Quant Researcher

Selby Jennings
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer - Trading Platform - Global Quant Firm

Data Engineer - Trading Platform - Global Quant Firm

Data Engineer Founding Role...

Machine Learning Quant - Start Up

Machine Learning Quant - Start Up...

(Senior) Forecasting Data Scientist (m/f/d)

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.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.