Quantitative Researcher - Data Curation

We are seeking a highly analytical and detail-oriented Quantitative Researcher to join our dynamic research team. The ideal candidate will have deep experience curating and analyzing a broad range of trading-related data sources-including traditional market data, fundamental datasets, and other vendor-supplied information. This role will contribute to the development of innovative trading strategies, support data-driven decision-making, and collaborate closely with trading, technology, and data acquisition teams.
Key Responsibilities:

• Curate, cleanse, and validate large volumes of market data, focusing on US equity and equity options
• Integrate, preprocess, and evaluate fundamental & alternative data sources
• Work closely with data acquisition & global data team to assess data quality
• Build and maintain robust data pipelines for research and live trading environments
• Perform data analysis ad statistical modeling to identify patterns and inefficiencies in the market
• Ensure the accuracy, completeness, and timeliness of datasets used in quantitative modeling.
• Document research processes, data provenance, and results with high standards of clarity and reproducibility
• Collaborate with software engineers and traders to translate research into production-grade models and tools
• Conduct quantitative research and analysis to support and enhance trading strategies

Required Skills & Experience:

• 3+ years of experience in a quantitative research or data-focused role in financial markets, ideally in a systematic trading environment
• Proven experience working with a diverse range of trading and financial data
• Must have previous knowledge of options markets and experience working with options data
• Hands-on experience integrating and analyzing non-market data sources is a plus
• Strong understanding of data vendor landscape
• Advanced proficiency in Python
• Experience with databases (SQL) and handling large datasets efficiently
• Familiarity with real-time data systems and tick-level data processing
• Familiarity with statistical and machine learning techniques
• Exceptional attention to detail and a systematic approach to problem-solving
• Strong written and verbal communication skills

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