At a Glance
- Tasks: Solve complex trading problems and build high-performance applications.
- Company: Join a dynamic team in the commodities trading sector.
- Benefits: Competitive salary, learning opportunities, and a collaborative work environment.
- Other info: Great chance to grow your career in a fast-paced industry.
- Why this job: Make a real impact on trading success with cutting-edge technology.
- Qualifications: Degree in a quantitative field and strong Python programming skills.
The predicted salary is between 63000 - 77000 £ per year.
THE ROLE
We are seeking a highly skilled and motivated Quantitative Engineer with a focus on Commodities Trading to join our business.
You will be part of a small but growing team where you will work directly with the business on complex technology trading problems.
You will collaborate with cross-functional teams, including analysts, traders and software engineers to have a real impact of the future success of our business.
THE ROLE
We are seeking a highly skilled and motivated Quantitative Engineer with a focus on Commodities Trading to join our business.
You will be part of a small but growing team where you will work directly with the business on complex technology trading problems.
You will collaborate with cross-functional teams, including analysts, traders and software engineers to have a real impact of the future success of our business.
Key Responsibilities
- Work closely with the trading desks and the Risk division to acquire in-depth business knowledge, identify their challenges, and capture specific needs.
- Design and build scalable, high-performance end-to-end applications and implement quantitative models for structuring deal evaluations, trading and risk management.
- Implement robust applications to replace Excel-based processes where applicable, to enhance reliability, reduce operational complexity and minimise operational risk.
- Contribute to the development of the shared quantitative platform designed to support all trading desks.
- Utilise cutting-edge technologies, such as cloud computing, to drive improvements in efficiency and productivity for the trading desk.
- Take ownership of the delivery and maintenance of high-quality code and provide prompt, effective support to resolve issues as they arise.
Qualifications and Requirements
- Degree educated in either Computer Science, Mathematics, Statistics, Financial Engineering, or a related quantitative field.
- Strong programming skills and advanced proficiency in Python.
- Understanding of probability theory and derivative option pricing.
- Experience with front-end frameworks such as Angular or React, and understanding of database management, including SQL or No SQL systems.
- Experience with Continuous Integration/Continuous Deployment (CI/CD) practices and cloud computing platforms.
- Intellectual curiosity and willingness to learn new models and physical commodity markets.
- Excellent problem-solving skills and a strong attention to detail.
- Strong stakeholder communication skills.
- Prior experience in commodities trading is beneficial but not required.
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Quant Engineer employer: Glencore
Glencore is an exceptional employer for graduates seeking to launch their careers in the dynamic world of energy commodities trading. Our London office offers a vibrant work culture that fosters innovation and collaboration, providing ample opportunities for professional growth and development. With a commitment to nurturing talent and a focus on entrepreneurial spirit, we empower our employees to make impactful contributions while enjoying the benefits of working within a global leader in natural resources.
StudySmarter Expert Advice🤫
We think this is how you could land Quant Engineer
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Quant Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Glencore, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Glencore. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Glencore
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Glencore!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
