Job Description

Your mission is to

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.

It could be a match if you have

• 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.