Quantitative Researcher

Company Description

Seven Alpha Investors Private Limited is a multi-strategy proprietary trading firm specializing in disciplined mathematical and statistical methods to deliver consistent returns. The firm operates across equities, futures, options, commodities, and currencies, leveraging an ultra-low-latency infrastructure built with C++ for fast and reliable market access. Based in Gurugram, the company fosters a collaborative work environment where team members share insights, uphold risk controls, and respond dynamically to live markets. With cutting-edge simulation and execution infrastructure, Seven Alpha offers an environment to learn from experienced quantitative professionals while testing and implementing innovative ideas.

Role Description

This is a full-time on-site role located in Gurugram for a Quantitative Researcher. The Quantitative Researcher will conduct in-depth research and analysis using mathematical and statistical techniques to develop and optimise high frequency quantitative trading strategies. Responsibilities include identifying patterns in financial data, creating predictive models, and collaborating with traders and developers to implement strategies. The role also involves back-testing strategies, conducting simulations, and improving algorithmic trading systems in response to market demands.

Qualifications

• Proficient knowledge in Statistics and Mathematics to analyse datasets and develop predictive models
• Strong foundation in Quantitative Research and Quantitative Investing with experience in creating data-driven trading strategies
• Excellent Analytical Skills and problem-solving ability, with a keen interest in financial markets and quantitative methods
• Strong proficiency in programming languages such as Python and C++ is required
• Capability to work effectively in an onsite environment, maintaining strong communication and teamwork under dynamic market conditions
• Advanced degree (Master's or Ph.D.) in Mathematics, Statistics, Computer Science, Financial Engineering, or a related field is preferred
• Experience with low-latency trading systems and simulation engines is a plus