Data Quality Project Lead - AI Robotics

Doist; San Jose (CA); On-site; USD 100,289,280 - 114,616,320; Full time

Job summary

Figure is seeking a Project Coordinator in Data Quality to own complete project lifecycles—from defining review guidelines to managing audit cycles, analysts, and vendors.

You will translate requirements into actionable guidance, oversee edge cases, and help shape calibration programs. The role blends data quality leadership with vendor and cross‑team collaboration to maintain high standards.

Qualifications

• 4+ years in data quality, labeling ops, or content review with end-to-end ownership.
• Experience writing review guidelines, acceptance criteria, or SOPs.
• Experience running audit/QA cycles and translating findings into changes.
• Able to work with ML/engineering to translate requirements into guidance.
• Vendor management experience including SLA communication.
• People management experience or readiness to take on direct reports.

Responsibilities

• Own review guidelines and acceptance criteria for your project; handle escalated edge cases.
• Contribute to golden set construction for your project type.
• Run project-level audit cycles and feed findings back into guideline updates.
• Coordinate analysts across projects within your scope.
• Write and maintain project-level SOPs; update them through retros.
• Manage direct reports where applicable, including performance reviews.
• Design project-specific training materials and calibration exercises.
• Serve as the primary contact for project-level vendor coordination, including SLA communications.
• Translate quality requirements into actionable review guidelines; act as a first contact for clarifications.

Skills

Data quality experience

Data labeling operations

Content review

Project ownership

Stakeholder communication

Job description

Figure is seeking a Project Coordinator in Data Quality to own complete project lifecycles—from defining review guidelines to managing audit cycles, analysts, and vendors.

You will translate requirements into actionable guidance, oversee edge cases, and help shape calibration programs. The role blends data quality leadership with vendor and cross‑team collaboration to maintain high standards.