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- Tasks: Design and optimise algorithmic trading strategies for global commodity markets.
- Company: Join a leading global hedge fund at the forefront of financial innovation.
- Benefits: Enjoy a full-time role with opportunities for growth and collaboration with top experts.
- Why this job: Make a significant impact in mid-frequency trading while working in a dynamic, innovative environment.
- Qualifications: Advanced degree in a quantitative field and strong programming skills required.
- Other info: Ideal for those passionate about algorithmic trading and machine learning.
The predicted salary is between 42000 - 84000 £ per year.
Get AI-powered advice on this job and more exclusive features.
A leading global hedge fund is seeking an experienced Quantitative Researcher to join their systematic commodities team in London. This role will focus on mid-frequency trading, with responsibility for the design, implementation, and optimization of advanced trading strategies across global commodity markets.
Responsibilities:
- Design, implement, and optimize mid-frequency algorithmic trading strategies for commodity markets including energy, metals and ags.
- Work alongside the PM with a focus on alpha generation, model implementation, backtesting and portfolio construction.
- Work closely with leading quantitative researchers and engineers to improve existing strategies and identify new trading opportunities.
Qualifications:
- Advanced academic qualifications (Master\’s/PhD) in a quantitative field, such as Mathematics, Physics, Statistics, Computer Science, or a related discipline.
- Proven experience in generating alpha and developing high-performing strategies within commodity markets
- Strong background in quantitative trading, with specific expertise in mid-frequency commodity strategies.
- Extensive proficiency in programming languages including Python
- Deep expertise in machine learning techniques and tools, with a focus on their application in strategy development and optimisation.
This position offers an exceptional opportunity for a seasoned quantitative researcher to make a significant impact within mid-frequency commodity markets. If you are driven by the pursuit of innovation in algorithmic trading and are looking for a challenging, high-impact role, we invite you to apply.
Seniority level
-
Seniority level
Mid-Senior level
Employment type
-
Employment type
Full-time
Job function
-
Job function
Finance, Research, and Engineering
-
Industries
Financial Services, Investment Management, and Capital Markets
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Quantitative Researcher - Commodities employer: Algo Capital Group
Contact Detail:
Algo Capital Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher - Commodities
✨Tip Number 1
Network with professionals in the commodities trading space. Attend industry conferences, webinars, or local meetups to connect with others who work in quantitative research and trading. This can help you gain insights into the latest trends and potentially lead to referrals.
✨Tip Number 2
Stay updated on market trends and developments in the commodities sector. Follow relevant news sources, subscribe to industry reports, and engage with online forums. This knowledge will not only enhance your understanding but also provide you with talking points during interviews.
✨Tip Number 3
Showcase your programming skills by working on personal projects or contributing to open-source initiatives related to algorithmic trading. This practical experience can demonstrate your capabilities and passion for the field, making you a more attractive candidate.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and algorithm questions specific to quantitative research. Familiarise yourself with common machine learning techniques and their applications in trading strategies, as this knowledge will be crucial during the interview process.
We think you need these skills to ace Quantitative Researcher - Commodities
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your advanced academic qualifications and relevant experience in quantitative research, particularly in commodities. Emphasise your programming skills, especially in Python, and any machine learning techniques you've applied.
Craft a Strong Cover Letter: In your cover letter, express your passion for algorithmic trading and how your background aligns with the responsibilities of the role. Mention specific examples of past projects where you designed or optimised trading strategies.
Showcase Your Technical Skills: Include a section in your application that details your proficiency in programming languages and machine learning tools. Highlight any relevant projects or achievements that demonstrate your ability to generate alpha in commodity markets.
Prepare for Technical Questions: Anticipate technical questions related to mid-frequency trading and quantitative strategies during the interview process. Brush up on your knowledge of backtesting, portfolio construction, and the latest trends in commodity markets.
How to prepare for a job interview at Algo Capital Group
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in programming languages, especially Python. Be prepared to discuss specific projects where you've implemented machine learning techniques and how they contributed to strategy development.
✨Demonstrate Your Understanding of Commodity Markets
Familiarise yourself with current trends and challenges in the commodity markets, particularly in energy, metals, and agriculture. Being able to discuss these topics will show your genuine interest and expertise in the field.
✨Prepare for Problem-Solving Questions
Expect to face technical questions that assess your analytical thinking and problem-solving abilities. Practice explaining your thought process clearly, as this will be crucial in demonstrating your quantitative skills.
✨Discuss Your Experience with Alpha Generation
Be ready to share specific examples of how you've successfully generated alpha in previous roles. Discuss the strategies you developed, the outcomes, and any lessons learned to showcase your impact in quantitative trading.
