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- Tasks: Conduct quantitative analysis and develop models to enhance trading strategies.
- Company: Join a leading quantitative hedge fund in the heart of Greater London.
- Benefits: Gain exposure to market dynamics and influence platform growth.
- Why this job: Be part of a collaborative team driving innovation in commodities trading.
- Qualifications: STEM degree, strong Python skills, and up to 3 years of experience.
The predicted salary is between 50000 - 70000 £ per year.
Anson McCade is seeking a Commodities Trading Analyst to join a leading quantitative hedge fund in Greater London. The role involves conducting quantitative analysis, developing supply and demand models, and enhancing analytical tools in a collaborative setting.
Ideal candidates have up to 3 years of experience, a STEM degree, and strong Python programming skills. This position offers significant exposure to market dynamics and the opportunity to influence the growth of the commodities platform.
Commodities Quant Trading Analyst — Data, Models & Growth in London employer: ANSON MCCADE
Contact Detail:
ANSON MCCADE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Commodities Quant Trading Analyst — Data, Models & Growth in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the commodities trading space on LinkedIn. A friendly message can go a long way in getting your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your quantitative analysis projects or any models you've developed. This will help you stand out during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your Python skills and be ready to tackle coding challenges. We recommend using platforms like LeetCode to sharpen your problem-solving abilities.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Commodities Quant Trading Analyst — Data, Models & Growth in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in quantitative analysis and Python programming. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements!
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 you can contribute to our team. Keep it concise but impactful – we love a good story!
Showcase Your Analytical Skills: In your application, include examples of how you've used data to drive decisions or improve processes. We’re looking for candidates who can demonstrate their analytical prowess, so don’t hold back!
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’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at ANSON MCCADE
✨Know Your Quantitative Analysis
Brush up on your quantitative analysis skills before the interview. Be ready to discuss specific models you've worked on and how they impacted decision-making. This shows you can apply your knowledge practically.
✨Show Off Your Python Skills
Since strong Python programming skills are a must, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice common algorithms or data manipulation tasks relevant to commodities trading.
✨Understand Market Dynamics
Familiarise yourself with current trends in the commodities market. Being able to discuss recent developments or shifts in supply and demand will show that you're not just technically skilled but also aware of the broader context.
✨Collaborative Mindset
This role emphasises collaboration, so be prepared to share examples of how you've worked effectively in teams. Highlight your communication skills and how you’ve contributed to group projects, especially in a quantitative setting.