Data Analysts

AutoRoboto is seeking a Data Analyst to join our engineering team in Mountain View, CA. This role will work with complex product, operational, automation, robotics, and testing-related datasets to help the company evaluate operational systems and make data-driven decisions. The Data Analyst will collect, validate, and analyze operational data; apply quantitative and statistical methods to define operational problems and evaluate alternatives; develop and test analytical models and decision-support workflows; create reports and dashboards; and collaborate with engineering to support analytical tools, automation workflows, and operational improvements.

The ideal candidate has a strong quantitative background, programming experience, and the ability to translate complex operational data into clear, actionable insights for technical and business stakeholders.

Responsibilities

• 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.

Qualifications

• Bachelor’s degree, or foreign equivalent, in Operations Research, Statistics, Mathematics, Computer Science, Data Science, Engineering, Business Analytics, or a closely related quantitative field.
• 2 years of experience in quantitative analytics, operations analysis, data analysis, data modeling, business analytics, product analytics, or a related analytical role. Additional related experience is preferred but not required.
• Experience using Python and SQL to query, clean, transform, analyze, and model data.
• Knowledge of statistical analysis, operations analysis, data modeling, data quality assessment, exploratory data analysis, and quantitative problem-solving methods.
• Experience creating reports, dashboards, charts, or visualizations using Tableau or similar business intelligence / visualization tools.
• Ability to communicate technical findings, operational tradeoffs, and recommendations clearly to both technical and non-technical stakeholders.
• Strong attention to detail, analytical judgment, and problem-solving ability.

Preferred Qualifications

• Experience or familiarity with big data or distributed data tools such as Spark, Hadoop, Cassandra, or similar technologies.
• Experience working with engineering teams on data pipelines, analytical prototypes, model validation, automation workflows, robotics workflows, or production data workflows.
• Experience analyzing product, operational, SaaS, automation, robotics, logistics, authorized penetration-testing, or security-assessment datasets.