At a Glance
- Tasks: Join our team to develop and enhance optimisation systems for global financial institutions.
- Company: LSEG, a leading global financial markets infrastructure provider with a commitment to innovation.
- Benefits: Enjoy competitive pay, healthcare, retirement planning, and paid volunteering days.
- Other info: Be part of a diverse team that values continuous improvement and innovative ideas.
- Why this job: Make a real impact on financial markets while growing your skills in a collaborative environment.
- Qualifications: 3-5 years of Python experience in data-intensive environments; strong problem-solving skills.
The predicted salary is between 50000 - 60000 £ per year.
LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a commitment to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs.
The Optimisation Services Engineering Team focuses on designing, building, maintaining and supporting optimisation services that help financial institutions reduce risk, improve capital efficiency, and operate more resiliently. Our work has a direct impact on the stability and efficiency of global financial markets.
We are looking for a mid-level quantitative developer / financial engineer to join our optimisation services team. In this role, you will contribute to building and enhancing systems that power large-scale optimisation runs used by global financial institutions. You’ll collaborate with colleagues in Software Engineering and Product Development to deliver reliable, high-quality solutions, while continuing to develop your expertise in quantitative methods and software engineering.
This is a product and client-focused role, where successful candidates will develop one or more of our services and will support live client optimisations. This is a collaborative role with opportunities to contribute both to strategic projects and continuous improvements, where your work will have visible and meaningful impact.
Examples of recent projects include:
- Implement improvements to our Interest Rates LCH Compression algorithm.
- Enhance our Counterparty Risk optimisation with new constraints and features.
- Work on adding support for Hedge Funds and Clearing Brokers in Initial Margin optimisations.
- Improve runtime performance of our core algorithms.
- Streamlining workflows and improving system architecture to reduce manual steps.
Key Responsibilities
- Contribute to the development and improvement of optimisation and analytics libraries.
- Work with teammates to understand and improve how data flows through our systems.
- Support optimisation runs alongside our Production team, helping ensure reliable execution.
- Explore ways to tune models and solutions to better meet client needs.
- Take part in both longer-term projects and shorter, iterative improvements.
- Collaborate with colleagues across teams to share knowledge and improve our products.
What We’re Looking For
We are interested in people with a strong foundation in quantitative development and a curiosity for solving complex problems. You might bring:
- 3-5 years of experience using Python in a data-intensive or numerical environment.
- Familiarity with software development practices such as testing, version control, and clean code design.
- Experience working with libraries such as pandas and numpy.
- Strong problem-solving skills and an interest in understanding how systems behave.
- Ability to communicate technical ideas clearly to a range of audiences.
Desirable
Experience in any of these areas would be a plus:
- Optimisation techniques (e.g., linear programming, mixed-integer programming, convex optimisation).
- Experience with optimisation libraries or modelling tools.
- Understanding of financial products, derivatives, or risk concepts.
- Exposure to UNIX/Linux or cloud environments such as AWS.
- A background in mathematics, statistics, or related fields.
Working With Us
You’ll work in a collaborative, supportive team where knowledge sharing is encouraged. You’ll have opportunities to learn and deepen your expertise in both quantitative methods and software engineering. You’ll be involved in solving real-world problems with tangible impact on financial markets. We value continuous improvement—both in our systems and in how we work together.
If you’re excited about this role and feel that you could grow into it, we encourage you to apply. We welcome applicants from a wide range of backgrounds and experiences, and we are committed to building an inclusive environment where everyone can contribute and succeed.
LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth. Our purpose is the foundation on which our culture is built. Our values of Integrity, Partnership, Excellence and Change underpin our purpose and set the standard for everything we do, every day.
Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law.
You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives.
LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
Junior Quantitative Developer in London employer: London Stock Exchange Group
LSEG is an exceptional employer that fosters a collaborative and innovative work culture, where your contributions directly impact the stability of global financial markets. With a commitment to employee growth, you will have opportunities to deepen your expertise in quantitative methods and software engineering while enjoying tailored benefits such as healthcare and paid volunteering days. Join us in London, where diversity is celebrated, and your individuality is valued, making it a rewarding place to build a meaningful career.
Contact Details:
London Stock Exchange Group Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Junior Quantitative Developer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with current employees at LSEG. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. It’s a great way to demonstrate your quantitative prowess and coding chops.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python skills and be ready to tackle some real-world problems. Practice explaining your thought process clearly—communication is key in collaborative roles like this.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at LSEG.
We think you need these skills to ace Junior Quantitative Developer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Junior Quantitative Developer role. Highlight your experience with Python, data-intensive environments, and any relevant optimisation techniques. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re excited about this role and how your background makes you a great fit. Don’t forget to mention your problem-solving skills and your interest in financial markets.
Showcase Your Projects:If you’ve worked on any relevant projects, make sure to include them! Whether it’s optimising algorithms or working with libraries like pandas and numpy, we love to see practical examples of your work and how you’ve tackled complex problems.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s straightforward and ensures your application goes directly to us. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at London Stock Exchange Group
✨Know Your Quantitative Stuff
Brush up on your quantitative methods and optimisation techniques. Be ready to discuss how you've applied these in past projects, especially using Python with libraries like pandas and numpy. Showing a solid understanding of financial products and risk concepts will definitely impress.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of complex problems you've solved in previous roles. Think about how you approached the problem, the tools you used, and the impact of your solution. This will demonstrate your analytical thinking and ability to contribute to the team.
✨Communicate Clearly
Practice explaining technical concepts in simple terms. You might be asked to present your ideas to non-technical stakeholders, so being able to communicate effectively is key. Use examples from your experience to illustrate your points.
✨Be Ready to Collaborate
Since this role involves working closely with various teams, be prepared to discuss how you've successfully collaborated in the past. Highlight any experiences where you shared knowledge or contributed to team projects, as this aligns with their emphasis on teamwork and continuous improvement.
