Top Automotive Data Analysis Jobs

Support Auto Glass Now leadership by translating business questions into structured analyses, validating data, and delivering actionable recommendations. Build and maintain advanced Excel forecasting models, document business logic and KPIs, extract and validate data using SQL, and communicate insights to varied audiences. Collaborate cross-functionally to inform capacity planning and operational decision-making.

Drive operational improvements for mapping tools by performing root-cause analyses, synthesizing user research, partnering with engineering on tests and automation, establishing KPIs, and designing A/B experiments to inform product and process priorities.

As an Operations Analyst at Carvana, you'll analyze data to drive insights, improve processes, and communicate findings across teams, utilizing SQL and visualization tools.

Lead development of large-scale data analysis and validation workflows for autonomous driving ML. Design metrics, build datasets, run statistical error and regression analyses, collaborate with ML and software teams, and maintain AWS/Kubernetes orchestration for large-scale evaluation.

The Inventory Data Analyst manages inventory to maximize profits by consulting on best practices, analyzing data trends, and enhancing inventory systems. Responsibilities include creating dashboards, building forecasting models, delivering reports, and conducting training. This role collaborates with teams to improve operational efficiencies.

Analyze pricing and market trends across products, regions, and dealers; build data models and reports for pricing recommendations, forecasts, lost-order analysis, and market participation; maintain quoting data integrity; support sales agreements; and assist development of ML tools to automate customer comparisons.

The Data Analysis Intern will preprocess and analyze training data for LLM projects, assist in training language models, and develop Python code to interact with APIs.

As a Data Analyst, you'll collect and preprocess datasets, perform feature engineering, and collaborate on data pipelines while generating insights through reports and visualizations.