Data Scientist

About WorldQuant

WorldQuant specializes in designing and implementing systematic financial strategies across diverse asset classes and global exchanges. Our proprietary research platform is used to generate high-quality predictive signals, known as alphas, which form the basis for market inefficiency-focused strategies. We foster a collaborative culture combining academic rigor with tangible results, encouraging open discussion, critical thinking, and continuous refinement of our approaches.

At WorldQuant, we prioritize intellectual excellence and exceptional talent. We seek forward-thinking individuals capable of innovating and shaping the future of quantitative finance.

Role Overview

This unique opportunity places you in a dynamic intraday team where you'll work alongside quantitative researchers, data engineers, and other specialists. Your focus will be researching, engineering, and validating quantitative signals derived from high-frequency equity market data spanning multiple global markets. You will oversee the entire lifecycle of each signal — from hypothesis generation about predictive market behaviors, feature engineering, data-based validation, to deployment within our research platform. The role combines deep research with hands-on coding and implementation within our proprietary framework.

Key Responsibilities

• Extract and engineer features from raw high-frequency market data, converting behavioral hypotheses into measurable quantitative signals.
• Integrate signals into our simulation and backtesting frameworks, iterating via exploratory data analysis and framework deployment.
• Conduct rigorous backtesting of features across historical datasets, evaluating performance in diverse markets, varying conditions, and edge cases such as market open/close and low-liquidity periods.
• Collaborate closely with research and engineering teams to synchronize implementation methods, validation criteria, and research strategies.
• Investigate novel and existing data sources to discover promising signal candidates for development.
• Attain subject matter expertise on deep learning and machine learning applications for high-frequency data, comprehending market microstructure and behavioral nuances.
• Generate insights from data and cooperate with the research team to develop actionable, tradable strategies.
• Create tooling to automate and optimize software development, testing, and deployment workflows.

Qualifications & Skills

• A bachelor's degree or higher in a quantitative or technical discipline.
• Proven experience in data science, particularly transforming noisy, real-world data into validated and stable signals or predictive models.
• Strong quantitative programming capabilities, including writing precise, production-grade, and performance-focused code; familiarity with low-latency programming implementations. Experience with C++ is advantageous.
• Knowledge or strong aptitude to quickly learn financial markets, trading, and market data behavior; prior understanding of market microstructure, dark pools, and exchange data is a plus.
• Practical and theoretical expertise with deep neural networks and machine learning techniques applicable to the high-frequency trading domain is beneficial.
• Ability to make pragmatic modeling decisions amid uncertainty, substantiating choices clearly through data.
• Excellent analytical and problem-solving skills, coupled with meticulous attention to detail.

Additional Information

Applicants acknowledge and consent to the company's privacy policy, which details the collection, usage, disclosure, retention, and protection of personal data along with associated legal rights. Policies may vary by jurisdiction.

WorldQuant is an equal opportunity employer committed to diversity and does not discriminate on any legally protected characteristics.