At a Glance
- Tasks: Research and develop predictive trading signals and models to enhance performance.
- Company: Leading quantitative trading firm with a focus on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement potential.
- Why this job: Join a dynamic team and make a real impact in the trading world.
- Qualifications: Experience in programming languages like Java, C++, or Python is essential.
The predicted salary is between 60000 - 80000 £ per year.
Harrington Starr is seeking a Research Software Developer to join a leading quantitative trading firm. This role involves researching and developing predictive signals and trading models to improve trading performance. You will collaborate with researchers and engineers throughout the lifecycle of the project, focusing on building scalable and maintainable trading strategies using programming languages such as Java, C++, or Python.
Quant Research Software Engineer - Trading Signals in London employer: Harrington Starr
Join a leading quantitative trading firm that values innovation and collaboration, offering a dynamic work culture where your contributions directly impact trading performance. With a focus on employee growth, you will have access to continuous learning opportunities and the chance to work alongside top-tier researchers and engineers in a vibrant location that fosters creativity and excellence.
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We think this is how you could land Quant Research Software Engineer - Trading Signals in London
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We think you need these skills to ace Quant Research Software Engineer - Trading Signals in London
Some tips for your application 🫡
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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✨Brush Up on Your Statistics
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