At a Glance
- Tasks: Develop and deploy trading strategies in commodities markets using advanced quantitative methods.
- Company: Leading proprietary trading firm with a global presence and innovative culture.
- Benefits: Competitive salary, performance bonuses, and investment in research infrastructure.
- Other info: Collaborative environment with opportunities for professional growth and direct PnL participation.
- Why this job: Join a dynamic team and make a real impact on trading strategies and market performance.
- Qualifications: Master's or PhD in a quantitative field with 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.
The role involves 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.
What we are looking for:
- 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.
Nice to have:
- 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.).
What's on offer:
- 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 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 strategies while working alongside experienced traders and engineers. Join us to leverage your quantitative skills in a dynamic setting that values innovation and excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher- commodities
✨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 develop and backtest those trading strategies, so bring your A-game and be ready to discuss your past successes.
✨Ask Smart Questions
During interviews or networking chats, ask insightful questions about their trading strategies and market approaches. This shows you’re genuinely interested and have done your homework. Plus, it gives you a chance to demonstrate your knowledge of commodities markets and statistical methods.
✨Apply Through Our Website
Don’t forget to apply directly through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to engage with us directly.
We think you need these skills to ace Quantitative Researcher- commodities
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of a Commodities Quantitative Researcher. Highlight your relevant experience in quantitative research, especially in commodities, and showcase any specific projects or strategies you've worked on that align with our needs.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about commodities trading and how your skills can contribute to our team. Be sure to mention any unique insights or experiences that set you apart from other candidates.
Showcase Your Technical Skills:Since we're looking for strong Python skills, make sure to highlight your programming experience. If you have knowledge of C++, Rust, or Julia, don’t forget to mention that too! Include any relevant projects or research that demonstrate your technical prowess.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. This way, we can easily track your application and ensure it gets the attention it deserves. 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 natural gas 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. Bring examples of your past work, especially any production-grade code you've written. If you have experience with C++, Rust, or Julia, mention it! This could set you apart from other candidates.
✨Backtesting is Key
Be ready to talk about your experience with backtesting and statistical validation. Discuss how you've approached overfitting and capacity issues in your previous projects. Providing concrete examples of how you've iterated on models based on live performance will impress the interviewers.
✨Communicate Clearly
Strong communication skills are essential for this role. Practice explaining complex quantitative concepts in simple terms. Be honest about your results and show that you're open to questioning your own findings. This will demonstrate your intellectual honesty and collaborative spirit.
