Quantitative Research Intern

About the Role

Quantitative Research Intern at QuantaNova Technologies LLP

• Duration: 2-4 Months (extendable/ convertible to full-time)
• Work Mode: Part-time/Full-time, Hybrid
• Location: Golf Course Road, Gurugram, Haryana

Key Responsibilities

• Contribute to the research, design, and implementation of a large number of diverse alpha signals on our proprietary backtesting and execution infrastructure.
• Analyze vast amounts of market data and extract market mispricing using core statistical methods.
• Assist in the formulation of Machine Learning frameworks critical for strategy optimization.
• Stay up-to-date with the latest research in quantitative finance, Financial Economics, and Signal Processing.

Requirements & Tech Stack

• Currently a B.Tech/M.Tech student in Computer Science, Mathematics, or a related engineering discipline from a Tier-1 engineering institute.
• Strong Python skills with a solid understanding of data structures and algorithms.
• Exceptional grasp on statistical and probability theory, time-series principles, regression, and linear algebra.
• Experience working with large datasets and associated big-data analysis libraries.
• Demonstrated knowledge of core ML tools and their theoretical underpinnings.
• Strong preference for candidates with prior experience working on alpha research (through internships, part-time work, global research roles, or quant competitions).

What We Offer

• Be part of a fast-growing team aiming to reshape the financial landscape with constant mentorship and guidance from senior researchers.
• Hands-on experience with real-world, end-to-end design of trading signals and strategies on a fully proprietary research infra.
• Ability to design vast arrays of novel signals and custom strategies across datasets and trading horizons, and work on diverse aspects of the trading system.
• Flexibility to work remotely & part-time during college schedules.

To Apply: Email your resume to nikunj.agarwal@quantanovatech.com, specifying your preferred work mode (Part-Time vs Full-Time / Remote vs in-person).