Data Scientist

About this job

WorldQuant is seeking a Data Scientist to join its new and rapidly growing intraday team in New Delhi. WorldQuant develops and deploys systematic financial strategies across global markets and asset classes by using a proprietary research platform to produce high-quality predictive signals, known as alphas. In this role, you will be part of a collaborative, results-oriented culture that combines academic sensibility with practical problem-solving. Your daily work involves partnering with quantitative researchers, data engineers, and technologists to research, engineer, and validate quantitative signals derived from high-frequency equity market data. You will handle the entire lifecycle of a signal, which includes forming hypotheses about market behavior, engineering features, validating them through historical backtesting, and implementing your code within the internal production research platform. It is a research-focused position that combines heavy hands-on coding, exploratory data analysis, and close collaboration with multidisciplinary teams to drive the production of financial strategies.

Responsibilities

• Research and engineer features from raw, high-frequency market data to translate hypotheses into quantitative signals.
• Implement signals inside the internal simulation and backtesting framework through exploratory data analysis.
• Validate features via historical backtesting across various markets, regimes, and edge cases.
• Collaborate with engineering and research teams to align on implementation approaches and validation standards.
• Explore new and existing data sources to identify candidate signals for further development.
• Become a domain expert on machine learning and deep learning applications for high-frequency data and market microstructure.
• Develop utility tools to automate software development, testing, and deployment workflows.

Requirements

• Minimum of a bachelor’s degree in a technical or quantitative field.
• Strong data science background with experience converting noisy real-world data into validated models or signals.
• Rigorous quantitative programming skills to write accurate, performance-aware code.
• Working knowledge of financial markets and trading data, or a strong aptitude to learn it quickly.
• Ability to make pragmatic modeling tradeoffs under ambiguity and articulate reasoning with data.
• Exceptional analytical, problem-solving abilities, and strong attention to detail.

Good to have

• Prior experience with C++.
• Prior market microstructure knowledge and experience with trading, exchange, and dark pool data.
• Practical experience and theoretical understanding of deep neural networks and machine learning techniques in high-frequency domains.

Who should apply

This role suits analytical candidates with a strong quantitative and technical background who enjoy tackling complex, ambiguous problems using data. It is ideal for professionals experienced in turning noisy real-world data into validated models, and those who thrive in a collaborative environment bridging finance, research, and software engineering. The posting does not explicitly mention whether freshers can apply, but requires a minimum of a bachelor's degree in a technical or quantitative field alongside relevant data science and programming skills.

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