At a Glance
- Tasks: Develop Python applications and quantitative tools for trading and research in Commodities.
- Company: Join Qube Research & Technologies, a global leader in quantitative investment management.
- Benefits: Enjoy a healthy work-life balance, competitive salary, and opportunities for professional growth.
- Other info: Collaborative culture that values diversity and innovation.
- Why this job: Make an impact in the financial markets while working with cutting-edge technology and data.
- Qualifications: 2+ years of software development experience, strong Python skills, and a passion for finance.
The predicted salary is between 56700 - 69300 £ per year.
Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world.
We are a technology and data driven group implementing a scientific approach to investing.
Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges.
QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors.
You will work within a quantitative development function supporting research and trading activities across Commodities, including Agriculture, Metals and Energy.
The role focuses on developing Python applications, quantitative tools and data infrastructure that support trading decisions, research and risk management.
You will collaborate closely with Quantitative Researchers, Traders, Data Engineers and infrastructure specialists to deliver solutions across alternative data, pricing, backtesting and statistical modelling.
- Your future role within QRT
- Design, develop and deploy quantitative tools supporting trading and research, including pricing, backtesting, risk analysis and Pn L reporting
- Build Python applications and data pipelines to explore, integrate and analyse alternative datasets, including weather and cargo data
- Collaborate with Quantitative Researchers and Traders to translate trading ideas and business requirements into production applications and quantitative models
- Architect, develop and maintain cloud applications in collaboration with infrastructure engineers, using AWS and related technologies
- Develop and apply statistical and machine learning models to support research and trading across Agriculture, Metals and Energy
- Implement quantitative optimisation methods and automate research and trading workflows
- Monitor, troubleshoot and enhance existing trading and research infrastructure to ensure reliability and performance
- Your present skillset
- Minimum 2 years of professional software development experience, with strong Python programming skills or proficiency in another object oriented language
- Strong understanding of software design principles and experience developing maintainable applications
- Proficiency with Python numerical and data analysis libraries such as Num Py, Pandas, Polars and Matplotlib
- Solid SQL skills and experience working with relational databases
- Experience developing data processing pipelines, ideally using orchestration frameworks such as Airflow
- Familiarity with AWS or similar cloud platforms and infrastructure as code practices
- Good understanding of software development practices, including Git, unit testing, packaging and scripting
- Experience using AI development tools to support coding, debugging and software delivery
- Strong quantitative and analytical skills, with an interest in financial markets and trading
- Ability to work autonomously, manage priorities and collaborate effectively with Researchers, Traders and Engineers
- Previous experience developing quantitative tools or models within Commodities trading is beneficial
- Experience building dashboards and data visualisation applications using Plotly, Streamlit, Dash or htmx is advantageous
- Advanced AWS experience, including services such as Dynamo DB, RDS, S3, Lambda and AWS CDK, is beneficial
- Familiarity with Snowflake or similar data platforms is advantageous
- Experience with parallel or distributed computing frameworks such as Slurm and Ray is beneficial
- Experience developing statistical or machine learning models is advantageous
- Previous experience mentoring junior developers or interns is beneficial
QRT is an equal opportunity employer.
We welcome diversity as essential to our success.
QRT empowers employees to work openly and respectfully to achieve collective success.
In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.
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Quantitative Developer - Commodities (Python) in London employer: Quberesearchandtechnologies
At Qube Research & Technologies, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to excel in their roles. As a Networks & Infrastructure Leader based in London, you will benefit from unparalleled opportunities for professional growth, mentorship, and collaboration within a cutting-edge environment focused on innovation in low-latency trading. Join us to be part of a team that values your expertise and encourages you to lead with confidence while making a significant impact on our business success.
Contact Details:
Quberesearchandtechnologies Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Developer - Commodities (Python) in London
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We think you need these skills to ace Quantitative Developer - Commodities (Python) in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Quberesearchandtechnologies.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Quberesearchandtechnologies and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Quberesearchandtechnologies
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Quberesearchandtechnologies uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
