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- Tasks: Lead projects in electronic trading, develop algorithms, and enhance trading models.
- Company: Join a top-tier investment bank with a leading Electronic Execution team.
- Benefits: Enjoy competitive pay, mentorship opportunities, and a collaborative global environment.
- Why this job: Shape the future of trading while working with cutting-edge technology and talented professionals.
- Qualifications: Masters or PhD in a quantitative field; strong coding skills in C++, R, and Python required.
- 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 are 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:
- An Masters 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: MindMatch
Contact Detail:
MindMatch Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Snr. Quant Cash Equities Execution
✨Tip Number 1
Familiarise yourself with the latest trends in electronic trading and algorithmic solutions. This will not only help you understand the current landscape but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge about the industry.
✨Tip Number 2
Network with professionals in the Cash Equities space, especially those involved in electronic execution. Attend industry conferences or webinars, and connect with them on platforms like LinkedIn. Building these relationships can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Brush up on your coding skills in C++, R, and Python. Consider working on personal projects or contributing to open-source initiatives that demonstrate your ability to develop and maintain high-quality code. This practical experience can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your previous experiences leading projects and mentoring others. Highlight specific examples where you've driven enhancements or developed new models, as this aligns closely with the responsibilities of the role and shows your leadership capabilities.
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 have driven enhancements in trading algorithms or developed new models.
Showcase 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 showcase your work in developing analytic tools or trading models.
Highlight Leadership Experience: Since the role involves mentoring junior team members, be sure to highlight any previous leadership or mentorship roles you've held. Discuss how you’ve provided technical guidance and supported team development in your past positions.
How to prepare for a job interview at MindMatch
✨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 a solid understanding of Cash Equities trading products and market microstructure. Be ready to discuss how these concepts influence algorithm design and execution strategies.
✨Highlight Collaboration 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 diverse teams.
✨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 performance and enhancements.
