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- Tasks: Build high-performance trading models and data platforms using Python.
- Company: Join one of the world's elite trading firms with a stellar reputation.
- Benefits: Enjoy a generous salary, bonuses, and excellent career progression.
- Why this job: Work with cutting-edge technology and learn from top industry experts.
- Qualifications: 3+ years as a Quantitative Developer or in software engineering with Python experience.
- Other info: Hybrid working model available; apply directly for more details.
The predicted salary is between 57600 - 144000 £ per year.
Client: Scientific Quant Fund
Salary: £80,000 - £200,000 Base (+ Bonus)
Location: London
The role:
My client are seeking a talented Quantitative Developer to help build out high performance trading models and greenfield data platforms. As a Quantitative Developer in this team, you will work across a range of projects, within which you will have input on software development and architecture, as well as engaging with statistical research to shape how this team will operate for years to come. The organisation is one of the most elite trading firms globally and you can expect to find:
- Generous benefits and bonus package - Market Leading!
- Excellent career progression and the ability to learn from some of the best developers, researchers and traders on the planet.
- The opportunity to work with tech years ahead of competitors!
You will have:
- 3+ Years experience as a Quantitative Developer (or related field like Software Engineering).
- 3+ Years experience using Python.
- Strong understanding of data infrastructure.
- Experience writing software designed for performance on a HUGE scale.
- Strong understanding of high level system design.
Responsibilities:
- Leveraging large datasets to inform trading strategies and improve models.
- Extract, Clean, and Process Data for huge scale (100s of billions of records).
- Design and maintain data pipelines, ensuring data is structured efficiently for easy retrieval and analysis.
- Tune and optimise new and existing models for performance, ensuring they handle massive data volumes efficiently.
- Write high-performance code for trading algorithms that execute strategies based on real-time data analysis.
If you are a Quantitative Developer, please apply directly or email rdelaney@hunterbond.com for more information.
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Developer (Python) - Hybrid Working - £80,000 - £200,000 Base (+ Bonus)
✨Tip Number 1
Familiarise yourself with the latest trends in quantitative finance and trading technologies. Being well-versed in current methodologies and tools will not only boost your confidence but also demonstrate your commitment to staying ahead in this fast-paced industry.
✨Tip Number 2
Network with professionals in the quantitative finance space. Attend industry conferences, webinars, or local meetups to connect with potential colleagues or mentors who can provide insights into the role and the company culture at elite trading firms.
✨Tip Number 3
Brush up on your Python skills, especially in relation to data processing and performance optimisation. Consider working on personal projects or contributing to open-source initiatives that showcase your ability to handle large datasets and write efficient code.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and system design problems relevant to quantitative development. Focus on algorithms, data structures, and high-level system architecture to ensure you can effectively communicate your thought process during interviews.
We think you need these skills to ace Quantitative Developer (Python) - Hybrid Working - £80,000 - £200,000 Base (+ Bonus)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience as a Quantitative Developer, particularly your proficiency in Python and any relevant projects you've worked on. Emphasise your understanding of data infrastructure and high-level system design.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention specific projects or technologies that excite you about the opportunity to work with a leading trading firm. Relate your past experiences to the responsibilities outlined in the job description.
Showcase Relevant Projects: If you have worked on significant projects involving large datasets or high-performance code, be sure to include these in your application. Describe your role, the challenges faced, and the outcomes achieved to demonstrate your capabilities.
Proofread Your Application: Before submitting, carefully proofread your application materials. Look for any spelling or grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail, which is crucial in this field.
How to prepare for a job interview at LinkedIn
✨Showcase Your Python Skills
Make sure to highlight your experience with Python during the interview. Be prepared to discuss specific projects where you've used Python to solve complex problems, especially in relation to data processing and algorithm development.
✨Demonstrate Your Understanding of Data Infrastructure
Since the role requires a strong understanding of data infrastructure, be ready to explain how you've designed or maintained data pipelines in the past. Discuss any challenges you faced and how you overcame them to ensure efficient data retrieval and analysis.
✨Prepare for Technical Questions
Expect technical questions that assess your knowledge of high-level system design and performance optimisation. Brush up on concepts related to large-scale data handling and be ready to provide examples of how you've tuned models for performance in previous roles.
✨Engage with Statistical Research
Since the role involves engaging with statistical research, be prepared to discuss how you've applied statistical methods in your work. Show your enthusiasm for using data to inform trading strategies and be ready to share insights from your past experiences.
