Data Scientist

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.

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.