Service Analyst

What to Expect

Position Description:

Tesla’s Service Business Analytics team supports our mission by converting raw service data into actionable insights and automation. We are committed to hiring the world's best and brightest people who can sit at the intersection of service operations, data science, and customer experience.

We believe that small agile teams using modern development stacks can have an outsized impact in driving better decisions and automating core service processes. We are looking for an Analyst who is passionate about service operations, predictive maintenance, parts logistics, and business decision-making.

This analyst will be responsible for providing data & analytics about our Service and Delivery Operations, partnering across business teams to find and execute on the highest leverage opportunities to democratize data, conduct data-driven analysis, and automate manual processes. This could include developing data pipelines for vehicle health, enhancing our data warehouse semantic layer for service history, creating dashboards for Service Center Managers, conducting ad-hoc analysis on repair turnaround times, and automating processes that get us closer to real-time planning and predictive service execution.

This analyst will work side-by-side with "data specialists" such as data engineers, data scientists, and software engineers and will partner closely with Service stakeholders to ensure data capabilities are harnessed to deliver business value. Successful analysts on our team have come from both business and technical backgrounds but all share a growth mindset and are constantly improving their knowledge and skills through continuous learning.

What You’ll Do Responsibilities

• Perform rigorous analyses on large, complex service datasets (repair logs, parts inventory, technician logs, customer feedback) and provide strategic insights, hypotheses, and conclusions based upon findings to non-technical stakeholders (Service Managers, Parts Directors, etc.).

• Write SQL/Python scripts to aggregate and process data across multiple service data sources (Service CRM, ERP, Vehicle Telematics, Parts Systems).

• Build and maintain dashboards and data pipelines: Create clean, reliable, and scalable “single-source-of-truth” data sets by partnering with data quality and application engineering teams to address data issues in upstream systems and document data lineage, business logic, and KPI definitions (e.g., First-Time Fix Rate, Customer Wait Time, Parts Availability).

• Collaborate proactively with business stakeholders to analyze information needs and requirements and successfully deliver dashboards, visualizations, and ad-hoc reports on time based on business prioritization.

• Drive operational efficiency: Identify bottlenecks in the service workflow, analyze root causes of vehicle downtime, and propose data-driven solutions to improve technician utilization and customer satisfaction (NPS/CSAT).

What You’ll Bring Requirements

• Japanese and English proficiency.

• BS/MS in STEM or quantitative field (CS, Applied Statistics, Operations Research, Engineering) or BS/MBA in Finance, Economics, Business Analytics.

• A minimum of 2 years of experience in Business Analytics (preferably within Automotive, Manufacturing, or Service Operations).

• A minimum of 2 years of experience working with Python for data analytics and automation.

• Expert in writing SQL and familiar with multiple database systems (MySQL, Microsoft SQL Server, Presto). Knowledge of Vertica is nice to have.

• Experience managing a broad stakeholder base, prioritize effectively, and deliver at the speed of relevance in a high-pressure environment.

• Excellent problem-framing, problem-solving, and project management skills, willingness to be flexible and adapt to change.

• Ability to self-start and self-direct work in an unstructured environment, comfortable dealing with ambiguity.

• Passion for the Service Industry: A deep understanding of or strong interest in automotive service operations, parts logistics, or customer experience is highly preferred.