Data Scientist
Analyzes complex financial datasets to derive actionable insights for trading systems, monitors data health, and communicates findings to leadership. Requires 1+ years experience with SQL, Pandas, R, statistics, and a quantitative bachelor's degree.
About the job
Responsibilities
- Design and implement systems to ensure data correctness and monitor data health in data stores and live feeds
- Proactively identify abnormal production behavior and communicate them clearly to relevant stakeholders
- Perform extemporaneous analyses on research and production trading systems with leadership
- Harness financial expertise and statistical analysis to gain actionable insights into our production trading and research systems
- Design and implement analysis pipelines that automate those analyses found to be valuable for ongoing monitoring
Requirements
- 1+ years of applied end-to-end industry experience, including internships working with complex datasets, including curation, querying, aggregation, exploratory data analysis, and visualization
- Experience using statistical methods to analyze data, identify patterns, conduct root cause analysis, discover insights, and recommend solutions
- Ability to frame and answer questions mathematically
- Ability to infer useful forward-looking directions from results of retrospective analysis
- Fluency in managing, processing, and visualizing tabular data using a combination of SQL, Pandas, and R
- Basic software development skills and experience with bash, linux/unix, and git
- Ability to refine requirements from ambiguous requests to produce reports demonstrating excellence in communication
- Bachelor’s degree in a quantitative discipline (statistics, biostatistics, data science, computer science, or a related field)
Preferred Qualifications
- Master’s degree in a quantitative discipline
- Prior industry experience or displayed interest in finance, such as related academic projects, coursework in financial engineering, or industry internships
- Experience developing in a production-facing environment and familiarity with standard concepts and tooling, e.g., CI/CD, git, Airflow
Skills
SQL, pandas, R, Bash, Git, Linux, Statistics, Data Visualization, Airflow, CI/CD
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