Quantitative Data Scientist

• Partner closely with quantitative researchers to identify, evaluate, and acquire new datasets relevant to trading and market research initiatives.

• Design, build, and maintain reliable Python-based data pipelines for collecting, cleaning, transforming, and storing research data.

• Develop automated workflows and processes to support systematic trading research and strategy development.

• Create analytical frameworks and tooling to process large datasets and generate statistical insights.

• Build and maintain research databases, data models, and data quality monitoring processes.

• Perform exploratory data analysis and statistical investigations to support alpha generation and hypothesis testing.

• Collaborate with researchers to operationalize research methodologies into repeatable analytical workflows.

• Manage data infrastructure running on cloud or dedicated server environments, ensuring stability, reliability, and performance.

• Document data sources, pipeline architecture, methodologies, and analytical processes to support knowledge sharing and reproducibility.

• Stay current on emerging data sources, technologies, and quantitative research techniques relevant to financial markets and options trading.
• Circa 1-3 years of experience in data engineering, data science, quantitative research support, or a related technical role.

• Strong Python programming skills with the ability to write clean, maintainable, and efficient code.

• Experience building and maintaining automated data pipelines.

• Strong understanding of probability, statistics, and quantitative analysis.

• Solid mathematical foundation, including multivariable calculus, linear algebra, and statistical inference.

• Experience working with large datasets and relational databases.

• Demonstrated ability to translate research requirements into technical solutions.

• Experience working in Linux/server-based environments.

Desirable:

• Experience in financial markets, trading, or quantitative investing.

• Familiarity with options markets, derivatives, and volatility products.

• Experience supporting systematic trading or quantitative research teams.

• Knowledge of cloud infrastructure (AWS, GCP, Azure).

• Experience with time-series analysis and financial data.

• Exposure to machine learning techniques and predictive modeling.

• Experience working with alternative data sources.