WorldQuant is hiring a Data Scientist in New Delhi. This is a full time role with on-site work. Salary is not disclosed in the listing. The original listing was published on Worldquant and may include more detail about the shift, interview process and joining date. Confirm the salary, work mode and joining date directly with the employer before sharing any documents.
WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.
WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.
Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.
The Role: This is a highly unique opportunity for a Data Scientist to join a new and rapidly growing intraday team. In this role you will partner with our close-knit team of quantitative researchers, data engineers, technologists and data sourcing colleagues to research, engineer, and validate quantitative signals derived from high-frequency equity market data across multiple global markets, while owning the full lifecycle of a signal, forming a hypothesis about what market behavior predicts, engineering it into a feature, validating it with data, and shipping it into a production research platform. This is a research-focused data science role with meaningful hands-on coding and implementing signals within our internal framework.
Key responsibilities include
- Research and engineer features from raw, high-frequency market data, translating market behavior hypotheses into quantitative signals.
- Implement signals within our internal simulation/backtesting framework, iterating between exploratory data analysis and framework-based implementation.
- Validate features through backtesting across historical data, checking behavior across different markets, regimes, and edge cases (e.g., market open/close, low-liquidity periods).
- Collaborate with research and engineering teams to align on implementation approaches, validation standards, and research decisions.
- Explore new and existing data sources to identify candidate signals worth developing further.
- Becoming a domain expert on different deep learning and machine learning applications for high frequency data, analyzing & understanding the underlying dynamics, market microstructure and behaviors within the data.
- Develop insights based on the data and collaborate with the research team to generate tradable
- Developing the utility tools that can further automate the software development, testing and deployment workflow.
What You’ll Bring
- Strong academic background – minimum of a bachelor’s degree in a technical or quantitative field.
- Strong data science background, with experience turning noisy, real-world data into validated, well-behaved signals or models.
- Rigorous quantitative programming skills, with the discipline to write accurate, production-quality, and performance aware code, including low-latency implementations where needed. Prior experience with C++ is a plus.
- Working knowledge of financial markets and how trading/market data behaves, or strong aptitude to learn it quickly. Prior market microstructure knowledge and experience with dark pools, trading and exchange data is a plus.
- Practical experience with and theoretical understanding of deep neural networks and other machine learning techniques in high frequency domain is a plus.
- Comfortably making pragmatic modeling tradeoffs under ambiguity while clearly articulating the reasoning behind them with data.
- Exceptional analytical & problem-solving abilities, with a strong attention to detail.
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