Data Scientist

Responsibilities

Data Scientist responsible for harvesting insights from a complex array of data. Role involves data curation, analysis, interpretation, visualization, and communication of findings to research staff and executive leadership. Leverage industry experience to work with complex datasets—curation, querying, aggregation, exploratory data analysis, and visualization. Use statistical methods to analyze data, identify patterns, conduct root‑cause analysis, find insights, and recommend solutions. Infer forward‑looking directions from retrospective analysis.

Manage, process, and visualize tabular data using SQL, Pandas, and R. Refine requirements from ambiguous requests to produce reports that demonstrate excellent communication. 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 clearly to stakeholders. Perform extemporaneous analyses on research and production trading systems with leadership.

Harness financial expertise and statistical analysis to gain actionable insights into production trading and research systems. Design and implement analysis pipelines that automate valuable ongoing monitoring.

• Master’s degree or equivalent in Statistics, Finance, Mathematics, Data Science, or a related quantitative field and two years of related experience.
• 12 months of demonstrated experience conducting exploratory data analysis and statistical hypothesis testing across diverse data sources to derive actionable insights.
• 12 months of demonstrated experience collaborating with researchers, developers, and traders on production monitoring, system design, documentation, knowledge transfer, and project execution.
• 12 months of demonstrated experience exploring, visualizing tabular data using SQL and Python or R, and summarizing results into reports and presentations tailored for technical audiences.
• 12 months of demonstrated experience evaluating risk–return profiles and performance metrics of strategies across different asset classes (e.g., equities, bonds, futures, derivatives), leveraging pricing models and simulation/backtesting frameworks for strategy and model validation.
• Demonstrated experience developing and maintaining automated data‑analysis pipelines using Python or R (and Bash or equivalent shell scripting), with unit and integration testing;
  Git‑based version control on a hosted repository platform (e.g., Git Hub); workflow orchestration tools (e.g., Airflow, Prefect, Luigi); and production deployment and monitoring.
• Will accept experience gained before, during, or after Master’s program and experience gained concurrently.

$160,000 to $180,000 per year, full‑time, located in Berkeley, CA.