Data Analysts

Collect, query, clean, validate, and structure raw data from internal systems, product and operational data sources, authorized testing workflows, automation tools, and robotics-based data collection processes for downstream analysis, reporting, and modeling.

Assess data quality, completeness, consistency, and accuracy; identify anomalies, missing values, and data integrity issues; and recommend practical approaches for improving the reliability of operational data collection.

Perform exploratory, statistical, and operations analysis to identify trends, constraints, correlations, patterns, outliers, and drivers of key operational, product, or business metrics.

Develop, test, and refine quantitative models, analytical algorithms, and decision-support methods using Python, SQL, and statistical/data modeling techniques.

Evaluate model performance and alternative operational approaches using appropriate quantitative metrics; document assumptions, limitations, and expected operational impact.

Collaborate with the engineering team to support analytical prototypes, define data requirements, validate feature logic, test model outputs, and assist with production-ready analytical, automation, and data collection workflows.

Build reports, dashboards, charts, and visualizations to communicate findings clearly to engineering, product, operations, and business teams.

Translate analytical findings into actionable recommendations for operational, product, and business improvements, including improvements to data collection, automation, testing, and robotics workflows.

Prepare written summaries and presentations explaining methodology, findings, risks, operational tradeoffs, and recommended next steps.