Quantitative Researcher

As a Quantitative Researcher at Maven Securities US Limited, your main responsibilities include designing and developing trading models, developing base price valuation algorithms, and supporting key projects like option pricing and volatility modeling. You'll be expected to collaborate on challenging quantitative projects that directly impact trading performance, using your expertise in programming and data analysis to create predictive models and optimize trading strategies.

To qualify for the Quantitative Researcher role at Maven Securities US Limited, you must possess a Master’s degree in Financial Engineering, Applied Mathematics, Physics, or a related field, along with at least four years of experience in developing predictive models and verifying hypotheses using large datasets. Proficiency in programming with Python and C++, and experience with visualizing multi-dimensional data is also essential.

At Maven Securities US Limited, you can expect a friendly and informal work culture that values collaboration and innovation. As a Quantitative Researcher, you will be part of a highly committed employee-owned firm where your contributions are appreciated, and you have the freedom to express your ideas. The company's atmosphere encourages professional growth and offers plenty of opportunities to make a significant impact.

In your role as a Quantitative Researcher at Maven Securities US Limited, you will utilize a variety of tools and technologies, primarily programming languages such as Python and C++. You will also work with advanced algorithms for data analysis and visualization, as well as real-time performance measurement tools essential for improving trading strategies in options and futures markets.

The salary range for a Quantitative Researcher at Maven Securities US Limited is between $175,000 and $250,000, reflecting the significant expertise and contributions expected in this role. Along with a competitive salary, employees also enjoy a comprehensive benefits package, including medical, dental, vision insurance, and a 401k plan.

Be prepared to discuss specific projects where you have developed predictive models, detailing your methodology, the tools you used, and the outcomes. Highlight how these models affected trading strategies or decision-making processes.

Explain that data calibration ensures the accuracy of trading models by adjusting parameters based on live and historical market data. Use examples from your experience to illustrate the process and its critical role in optimizing trading performance.

Share your systematic approach to problem-solving, such as breaking down larger problems into manageable parts, utilizing data analysis, and collaborating with peers from diverse backgrounds to reach innovative solutions.

Discuss your proficiency in programming languages such as Python and C++, and provide examples of how you've applied these skills in past quantitative research projects, focusing on the impact of your work.

Select a relevant project that posed challenges in terms of data or complexity. Detail the steps you took to overcome these challenges, including any unique approaches or methodologies that led to a successful outcome.

Describe your validation process, which includes back-testing models against historical data and testing for robustness. Highlight the importance of this process in ensuring that your models are reliable and accurate.

Emphasize your communication skills and collaborative efforts, discussing tools or methodologies that you've found effective in fostering teamwork and knowledge sharing among diverse teams.

Discuss your strategies for ongoing education, such as attending relevant conferences, engaging in online courses, or participating in finance-related forums and communities to stay informed about the latest trends and technologies in the field.

Discuss the significance of volatility modeling in predicting market behavior and its vital role in risk management and pricing options. Use examples to illustrate how effective volatility models can enhance trading strategies.

Reflect on your passion for quantitative analysis and problem-solving. Talk about your enthusiasm for financial markets and how the opportunity to contribute to innovative trading strategies drives your motivation.