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- Tasks: Lead projects in electronic trading, enhancing algorithms and developing new models.
- Company: Join a top-tier investment bank with a renowned Electronic Execution team.
- Benefits: Enjoy a collaborative global environment and opportunities for mentorship.
- Why this job: Shape the future of electronic trading while working with cutting-edge technology.
- Qualifications: Master's or PhD in a quantitative field; strong coding skills in C++, R, and Python.
- Other info: Ideal for those passionate about finance and technology, eager to make an impact.
The predicted salary is between 43200 - 72000 £ per year.
My client, a T1 Investment bank with a renowned Electronic Execution team within the Cash Equities division, is looking for an experienced Quantitative Researcher to join them. In this role, you will play a critical part in shaping and advancing their electronic trading capabilities. You’ll collaborate with colleagues globally to share insights, improve platform interfaces, and ensure adherence to best practices. You’ll be responsible for leading key projects from start to finish, including designing and implementing new models and supporting the full platform lifecycle. You’ll also drive enhancements to our trading algorithms and deliver robust, high-quality solutions that keep us at the forefront of electronic execution.
Key Responsibilities:
- Drive and champion electronic execution initiatives within the Cash Equities team, fostering collaboration across global teams.
- Lead the development and deployment of new trading models and algorithmic solutions.
- Enhance and refine existing algorithms, adding new features and improving performance.
- Design and build analytic and implementation approaches, including system architecture and interfaces.
- Develop, test, and maintain high-quality C++, R, and Python code for new and existing tools.
- Create and support analytic tools for both team and business-wide use.
- Provide technical leadership and guidance, especially for complex issues requiring deep expertise and advanced analysis.
- Act as a mentor and escalation point for junior team members.
The ideal candidate will have:
- A Master’s level or above education in a quantitative discipline or a PhD in a scientific or engineering field with a strong interest in financial modelling.
- Deep understanding of Cash Equities trading products and market microstructure.
- Proven experience developing code in C++, R, and Python.
- Strong quantitative and analytical skills, with a background in numerical techniques and machine learning (both supervised and unsupervised).
Snr. Quant Cash Equities Execution employer: Bruin
Contact Detail:
Bruin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Snr. Quant Cash Equities Execution
✨Tip Number 1
Network with professionals in the finance and quantitative research sectors. Attend industry conferences, webinars, or local meetups to connect with people who work in electronic execution or cash equities. This can help you gain insights into the role and potentially get referrals.
✨Tip Number 2
Familiarise yourself with the latest trends and technologies in electronic trading. Follow relevant blogs, podcasts, and publications to stay updated on advancements in algorithmic trading and market microstructure, which will demonstrate your passion and knowledge during interviews.
✨Tip Number 3
Prepare to discuss your previous projects in detail, especially those involving C++, R, and Python. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will showcase your problem-solving skills and technical expertise.
✨Tip Number 4
Consider contributing to open-source projects related to quantitative finance or algorithmic trading. This not only enhances your coding skills but also demonstrates your commitment to the field and can serve as a talking point in interviews.
We think you need these skills to ace Snr. Quant Cash Equities Execution
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in quantitative research, electronic trading, and programming languages like C++, R, and Python. Emphasise any projects or roles that demonstrate your ability to lead initiatives and collaborate with global teams.
Craft a Compelling Cover Letter: In your cover letter, express your passion for electronic execution and how your background aligns with the responsibilities of the role. Mention specific experiences where you've developed trading models or enhanced algorithms, showcasing your technical leadership.
Showcase Your Technical Skills: Include examples of your coding proficiency in C++, R, and Python within your application. If possible, provide links to any relevant projects or GitHub repositories that demonstrate your ability to develop high-quality code and analytic tools.
Highlight Collaborative Experiences: Since the role involves collaboration across global teams, mention any past experiences where you successfully worked with diverse groups. This could include cross-functional projects or mentoring junior team members, which will show your ability to foster teamwork.
How to prepare for a job interview at Bruin
✨Showcase Your Technical Skills
Be prepared to discuss your experience with C++, R, and Python in detail. Bring examples of projects where you've developed trading models or algorithms, and be ready to explain your thought process and the outcomes.
✨Understand Market Microstructure
Demonstrate your knowledge of Cash Equities trading products and market microstructure. Be ready to discuss how these concepts influence electronic execution and the development of trading strategies.
✨Highlight Collaborative Experience
Since the role involves working with global teams, share examples of how you've successfully collaborated on projects. Emphasise your ability to communicate complex ideas clearly and work effectively in a team environment.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your quantitative and analytical skills. Practice explaining your approach to solving complex problems, especially those related to algorithm development and enhancements.
