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Data Analyst
Not specifiedFull-timeData ScienceFinTech
Analyze performance and behavior of production trading systems using large datasets. Conduct statistical research, investigate issues, and develop monitoring methods and analytical tools in collaboration with research, development, and operations teams.
Responsibilities
- Monitor trading-system performance, identify unusual behavior and trends, and investigate underlying causes.
- Analyze trading activity and system behavior to understand performance changes.
- Design and conduct statistical analyses and experiments to evaluate system performance and changes.
- Develop monitoring methods, indicators, and automated alerts while reducing noise and false alarms.
- Analyze historical data to distinguish normal variability from significant changes.
- Investigate production issues, identify root causes, and recommend actions.
- Turn large, complex datasets into actionable conclusions.
- Develop analytical tools, metrics, and datasets for research and operational teams.
- Build and maintain data pipelines as needed.
- Own analytical problems from defining the question and obtaining data through analysis, conclusions, and ongoing monitoring.
Requirements
- Bachelor's degree in computer science, mathematics, statistics, physics, engineering, or another highly quantitative field.
- Strong analytical and quantitative skills, including knowledge of statistics and statistical inference.
- Strong SQL skills and experience working with large datasets.
- Good Python programming skills, including experience with data-analysis tools and libraries.
- Ability to investigate complex systems, formulate hypotheses, and use data to identify root causes.
- Ability to communicate analytical findings clearly and collaborate with technical teams and stakeholders.
- Careful attention to detail and commitment to data quality and reliability.
Nice to have
- A relevant quantitative master's degree.
- Experience with experimentation, A/B testing, regression, time-series analysis, or other applied statistical methods.
- Experience building monitoring systems, metrics, dashboards, or automated anomaly detection.
- Experience with large-scale analytical databases, data-processing systems, or data pipelines.
- Familiarity with Linux and production software environments.
- Experience in quantitative research, algorithmic trading, or another data-intensive technology environment.