At a Glance
- Tasks: Develop and deploy systematic trading strategies in commodities markets.
- Company: Leading proprietary trading firm with a global presence.
- Benefits: Direct PnL participation, competitive salary, and collaborative environment.
- Other info: Strong focus on innovation and substantial investment in research infrastructure.
- Why this job: Make a real impact in trading with cutting-edge research and technology.
- Qualifications: Master's or PhD in quantitative discipline and 2+ years of relevant experience.
The predicted salary is between 60000 - 80000 £ per year.
We are a leading proprietary trading firm with a global presence across multiple asset classes. Our commodities business covers a broad universe of physical and financial commodities — across power, natural gas, crude oil and refined products, metals, emissions and agricultural markets — traded on exchange and OTC, on short- and medium-term horizons.
We are hiring a Commodities Quantitative Researcher to develop, backtest and deploy systematic trading strategies across one or more commodities markets. The seat sits within the research function and works closely with traders, quant developers and infrastructure engineers, with full ownership of ideas from initial hypothesis through to production deployment and live PnL.
Key responsibilities:- Develop alpha signals and systematic trading strategies across one or more commodities markets, combining price action, fundamentals, positioning, weather and alternative data.
- Conduct rigorous backtesting and statistical validation, with strong discipline around overfitting, regime sensitivity, capacity and transaction-cost realism.
- Translate research into production-grade code that integrates with the firm's trading systems.
- Work alongside traders on signal calibration, sizing logic, execution strategy and risk management.
- Monitor live strategy performance, attribute PnL and iterate continuously on model improvements.
- Contribute to the broader commodities research stack — data pipelines, feature libraries, backtesting framework — and share infrastructure improvements across the team.
- Master's or PhD in a quantitative discipline (mathematics, statistics, physics, computer science, engineering, financial engineering or similar).
- 2+ years of quantitative research experience in commodities at a proprietary trading firm, hedge fund, bank trading desk, energy trader or commodity merchant. Exceptional PhD graduates with directly relevant research will also be considered.
- Strong Python; working knowledge of C++, Rust or Julia is a plus.
- Hands-on experience with statistical and machine-learning methods — time series, regression, gradient boosting, dimensionality reduction; deep learning is a plus.
- Solid understanding of commodities markets — fundamentals, physical flows, exchange microstructure, futures and options pricing, contango/backwardation, seasonality and storage dynamics.
- Track record of taking research from idea to production with live performance attribution.
- Strong communication skills and the intellectual honesty to question your own results.
- Depth in a specific market: European or US power, natural gas, crude and refined products, base or precious metals, soft and grain agriculturals, or emissions.
- Experience with mid- or high-frequency execution, market microstructure, or order-book modelling.
- Fundamentals modelling background — supply-demand balances, storage, dispatch optimisation, refinery yields, weather and load forecasting.
- Familiarity with the relevant exchanges and venues (CME, ICE, EEX, EPEX, Nord Pool, LME, COMEX, Nodal, MISO / PJM / ERCOT nodal markets, etc.).
- Direct PnL participation with meaningful upside on successful strategies.
- A collaborative, low-bureaucracy research environment alongside senior trading and engineering colleagues.
- Substantial investment in research infrastructure — proprietary data, large-scale compute, mature backtesting framework and production deployment pipelines.
- Highly competitive base salary and discretionary bonus aligned to PnL outcomes.
Quantitative Researcher- commodities in London employer: DURLSTON PARTNERS
As a leading proprietary trading firm, we offer an exceptional work environment for Quantitative Researchers in commodities, characterised by a collaborative and low-bureaucracy culture. Our commitment to employee growth is evident through substantial investments in research infrastructure and direct PnL participation, allowing you to see the impact of your work on live strategies. Located in a vibrant financial hub, we provide access to cutting-edge resources and a dynamic team of experts, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher- commodities in London
✨Network Like a Pro
Get out there and connect with folks in the commodities trading scene. Attend industry events, join relevant online forums, and don’t be shy about reaching out on LinkedIn. We all know that sometimes it’s not just what you know, but who you know!
✨Show Off Your Skills
When you get the chance to chat with potential employers, make sure to highlight your quantitative skills and any relevant projects you've worked on. We want to see how you can apply your knowledge to real-world problems, so bring your A-game!
✨Ask Smart Questions
During interviews, don’t just wait for them to ask you questions. Show your interest by asking insightful questions about their trading strategies or market outlook. This not only demonstrates your knowledge but also shows that you’re genuinely interested in the role.
✨Apply Through Our Website
We encourage you to apply directly through our website. It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re proactive and really keen on joining our team!
We think you need these skills to ace Quantitative Researcher- commodities in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your quantitative skills and experience in commodities. We want to see how you've developed trading strategies or conducted backtesting in your previous roles. Be specific about the tools and methods you've used!
Tailor Your Application:Don’t just send a generic CV and cover letter. We love it when applicants tailor their applications to our job description. Mention specific commodities markets you’ve worked with and any relevant projects that align with what we do at StudySmarter.
Be Clear and Concise:When writing your application, clarity is key! We appreciate well-structured documents that get straight to the point. Avoid jargon unless it's necessary, and make sure your passion for quantitative research shines through.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at DURLSTON PARTNERS
✨Know Your Commodities
Make sure you brush up on your knowledge of the commodities markets. Understand the fundamentals, physical flows, and pricing dynamics. Being able to discuss specific markets like European power or crude oil will show that you're not just a numbers person but also have a solid grasp of the industry.
✨Showcase Your Coding Skills
Since strong Python skills are a must, be prepared to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code. If you have experience with C++, Rust, or Julia, mention it as it could give you an edge.
✨Backtesting is Key
Be ready to discuss your experience with backtesting and statistical validation. Prepare examples of how you've approached these tasks in the past, focusing on how you avoided overfitting and ensured realistic transaction costs. This will highlight your analytical skills and attention to detail.
✨Communicate Clearly
Strong communication skills are essential for this role. Practice explaining complex concepts in simple terms, as you'll need to collaborate with traders and engineers. Show that you can question your own results and engage in constructive discussions about your research.
