At a Glance
- Tasks: Develop and enhance optimisation platforms using Python for global financial markets.
- Company: Join Harrington Starr, a collaborative engineering team in London.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting challenges and career advancement.
- Why this job: Make a real impact by solving financial problems with cutting-edge optimisation techniques.
- Qualifications: Experience in Python development and a passion for financial engineering.
The predicted salary is between 60000 - 80000 £ per year.
Harrington Starr is seeking a mid-level Quantitative Developer / Financial Engineer to join its collaborative engineering team in London. You will develop and enhance large-scale optimisation platforms, writing production Python and improving performance while working with data-intensive datasets used in global financial markets.
The role focuses on building and supporting production systems, expanding functionality, and applying optimisation techniques to real-world financial problems.
Quantitative Python Engineer – Optimisation Systems employer: Harrington Starr
Join a leading high-volume futures trading organisation in London, where you will play a pivotal role in ensuring the stability of critical trading and clearing platforms. With a strong focus on employee growth and a collaborative work culture, this company offers competitive daily rates and long-term contract opportunities, making it an excellent employer for those seeking meaningful and rewarding careers in the capital markets sector.
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We think this is how you could land Quantitative Python Engineer – Optimisation Systems
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We think you need these skills to ace Quantitative Python Engineer – Optimisation Systems
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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