Data Scientist

India·Delhimid

Data ScientistData

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Quick Summary

Key Responsibilities

Research and engineer features from raw, high-frequency market data, translating market behavior hypotheses into quantitative signals.

Technical Tools

Data ScientistData

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.

Responsibilities

~2 min read

→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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