About this role
This Data Scientist role at WorldQuant is focused on developing and deploying systematic financial strategies by creating high-quality predictive signals, or 'alphas,' across various asset classes and global markets. The core objective of this position is to build the foundation of a balanced, global investment platform by identifying and engineering market inefficiencies. The team operates with a culture that values both academic rigor and accountability for tangible results, encouraging team members to challenge conventional thinking and continuously improve their methods.
The individual in this role will work closely with quantitative researchers, data engineers, and technologists. Their primary function involves owning the entire lifecycle of a quantitative signal: researching and engineering features from high-frequency market data, formulating hypotheses about market behavior, validating these signals through rigorous backtesting, and ultimately shipping them into the production research platform. This is a research-intensive role that requires a blend of deep analytical thinking and hands-on implementation skills.
This opportunity is ideal for someone who thrives on turning complex, noisy market data into actionable, validated quantitative insights. The ideal candidate possesses intellectual horsepower and the ability to balance theoretical knowledge with practical, production-quality coding. Success in this role depends on the ability to deeply understand market microstructure and apply advanced machine learning techniques to solve real financial problems.
What you will do
- Research and engineer features derived from raw, high-frequency market data to translate market behavior hypotheses into quantitative signals.
- Implement these developed signals within the internal simulation and backtesting framework, moving between exploratory data analysis and framework-based coding.
- Validate the engineered features by conducting backtesting across historical data, testing performance across different markets, regimes, and specific edge cases.
- Collaborate with research and engineering teams to establish alignment on implementation standards, validation protocols, and research decisions.
- Explore new data sources to identify potential candidate signals that warrant further development.
- Become a subject matter expert on deep learning and machine learning applications relevant to high-frequency data, focusing on market microstructure.
- Develop utility tools to automate the software development, testing, and deployment workflow for signals.
What you need
- A strong academic background, evidenced by a bachelor’s degree in a technical or quantitative field, is required.
- Demonstrated experience in data science, specifically in transforming noisy, real-world data into validated and well-behaved signals or models.
- Rigorous quantitative programming skills are necessary, including the discipline to write accurate, production-quality, and performance-aware code, especially for low-latency requirements.
- A working knowledge of financial markets and how trading and market data behave, or a strong aptitude to rapidly learn this domain.
- Prior experience with market microstructure, such as dark pools or trading/exchange data, is a plus.
- Practical experience with deep neural networks and other machine learning techniques in the high-frequency domain is beneficial.
- The ability to make pragmatic modeling tradeoffs under ambiguity while clearly articulating the rationale using data is essential.
Nice to have
- Prior experience with C++
- Prior market microstructure knowledge
Benefits
- WorldQuant is an equal opportunity employer
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The posting does not specify the remote work policy for this position.
What is the expected compensation?
The posting does not provide specific salary details for this role.
What is the primary technology stack used?
The role involves implementing signals within an internal simulation/backtesting framework and requires rigorous quantitative programming skills.
What is the team structure like?
The Data Scientist will partner with a close-knit team consisting of quantitative researchers, data engineers, technologists, and data sourcing colleagues.