Head of Data Collection Operations, Nimble Brain

Job description

Job responsibilities

• Own end-to-end data operations for Nimble's Brain: intake, collection, labeling, quality auditing, and delivery of training and evaluation datasets.
• Design and run the operational processes and staffing plans (in-house and vendor/BPO) needed to label and QA data at scale, on schedule, and within budget.
• Define and track operational metrics — throughput, accuracy, turnaround time, cost per label — and build the dashboards and reporting cadence to keep the org aligned.
• Build feedback loops between data quality issues and upstream fixes, partnering with research and engineering to close the loop quickly.
• Coordinate with on-site data collection teams across warehouse locations to ensure data capture protocols are followed consistently.
• Manage relationships and SLAs with external labeling/annotation vendors, including onboarding, training, and performance management.
• Identify and resolve bottlenecks in the data pipeline, escalating and driving cross-functional fixes where needed.
• Continuously improve tooling, documentation, and standard operating procedures for data operations as Nimble scales.

Job requirements

• 4+ years in data operations, labeling operations, business/technical operations, or a similar role managing high-volume operational pipelines.
• Track record building and running scalable operational processes, ideally involving data collection, annotation/labeling, or quality assurance.
• Comfortable managing vendor/BPO relationships and driving performance against SLAs.
• Strong metrics orientation — able to define KPIs, build reporting, and use data to drive operational decisions.
• Excellent cross-functional communication skills; able to align research, engineering, and operations stakeholders.
• Demonstrated ownership and comfort operating with ambiguity in a fast-paced, high-growth environment.
• Alignment with Nimble's values of being resourceful, humble, dependable, and committed to legendary impact.
• Experience in AI/ML data pipelines, data labeling platforms, or model evaluation workflows.