Service Analyst

Responsibilities

• Partner with the account team to understand contracted requirements, accuracy SLAs, and prioritization across the clients portfolio.
• Own tracking and reporting of account and fleet level performance across deliverables, accuracy, and timeliness.
• Perform detailed QA and root cause analysis to identify service gaps and systemic patterns.
• Document findings with precision; maintain a clear audit trail of issues, resolutions, and follow-ups.
• Flag issues and prioritize fixes with the engineering team; hold the thread through to resolution and verify that quality actually improved.
• Own and continuously improve the service observability handbook and standard operating procedures.
• Bring structure to recurring workflows so that nothing falls through the cracks across teams.
• Collaborate with the data-labeling team to forecast resource requirements given contracted accuracy SLAs.
• Collaborate with the Computer Vision, Solutions Engineering, Service Operations, and Client Success teams to configure the system to maximize service quality.
• Host office hours to drive adoption of service observability tools across the broader organization.
• Build and maintain dedicated dashboards and tooling (Looker, BigQuery, etc.) to monitor service health at the client and fleet level.
• Maintain a high standard of data accuracy and dashboard reliability.

Qualifications

• 2-5 years in a data analyst, operations analyst, QA analyst or similar role with a strong analytical and operational bent.
• Strong SQL and data querying skills. Comfortable working in BigQuery or equivalent cloud warehouse.
• Experience building and maintaining dashboards in BI tools (Looker preferred).
• Exceptionally organized, able to manage multiple workstreams, track open issues across teams, and keep documentation current without prompting.
• Highly attentive to detail: you notice when numbers don't add up, when a trend breaks pattern, or when a process step was skipped.
• A clear communicator across technical and non-technical audiences; able to translate data findings into crisp operational recommendations.