Zoomph operates Computer Vision workloads for sponsorship tracking. To ensure accuracy and data quality, human workforce programs support the AI pipeline to review missed logos and false positives from Computer Vision, and to manage human-in-the-loop labeling jobs.
This role is responsible for managing such workstreams end-to-end. This includes interfacing with internal stakeholders to understand data labeling priorities, translating those priorities into actionable tasks for the workforce, and ensuring timely, high-quality output.
STAKEHOLDER COLLABORATION & SCOPE DEFINITION
Work closely with the Zoomph Account Management and Customer Success teams to identify and prioritize data labeling needs across clients and campaigns.
- Gather requirements from Account Management and CS to understand which clients, brands, and campaigns require prioritized labeling effort.
- Define the scope of each work effort: identify which tasks need to be completed, which brands are in scope, and what the acceptance criteria are.
- Use internal agents and on-demand tools to analyze client workloads
- Translate business-level requirements into clear, actionable task briefs for the moderation and HITL workforces.
- Communicate scope, priorities, and timelines to the manual moderation team, ensuring alignment before work begins.
- Provide status updates and estimated completion timelines back to Account Management and CS stakeholders.
- Serve as the primary point of contact for workforce-related questions from internal teams; answer clarifying questions and manage expectation-setting.
QUALITY MODERATION WORKFORCE MANAGEMENT
Oversee the day-to-day operation of the moderation team responsible for reviewing false positives and missed detections that may impact client reporting.
- Assign moderation tasks to the team based on priority, capacity, and client urgency.
- Ensure the team understands the context and requirements of each task before beginning work.
- Monitor progress and track completion rates against committed timelines.
- Review output quality and provide feedback or clarification when the team encounters ambiguous cases.
- Identify bottlenecks or capacity constraints and escalate or resolve as needed.
- Maintain clear documentation of task assignments, completion status, and any issues encountered.
- Provide regular reporting to internal stakeholders on workforce throughput and task status.
DATA LABELING WORKFORCE MANAGEMENT
Own the end-to-end lifecycle of Human-in-the-Loop (HITL) jobs submitted for blank label resolution.
- Create new HITL projects as labeling needs arise.
- Configure HITL task submissions on a per-client basis, including task instructions, labeling schema, quality thresholds, and workforce routing.
- Monitor the backlog of submitted tasks and track completion rates and throughput against SLAs and client expectations.
- Review task output for quality and flag or remediate errors as needed.
- Respond to questions from the HITL workforce, providing clarifications on labeling instructions or edge cases.
- Scale workforce headcount up or down in coordination with workload demands and budget constraints.
- Provide timelines and completion estimates to stakeholders for active and upcoming HITL jobs.
KEY INTERFACES
Internal Teams:
- Account Management & Customer Success — source of client priorities, brand scope, and urgency signals, communicates client-facing data quality issues and deadlines
- Engineering / AI / Data Teams — coordination on Computer Vision AI improvements, pipeline behavior, edge cases, and tooling
External Workforces:
- Offshore Manager who oversees
- Quality Moderation Team (7-10 resources)
- HITL Workforce (variable headcount)
SUCCESS METRICS
- On-time completion rate of moderation and HITL tasks against committed timelines
- Labeling accuracy and quality scores across both workstreams
- Stakeholder satisfaction from Account Management and CS teams
- Backlog health: tasks awaiting assignment, in-progress, and completed
- Workforce utilization and efficiency (output per person-hour)
JOB REQUIREMENTS
Required:
- Bachelor's degree in Business, Data Science, Artificial Intelligence, Computer Science, or a related technical field; equivalent work experience will be considered
- 3-5 years of experience in AI Operations, data management, or program management roles with a focus on AI model training, deployment, or workload management.
- Experience managing remote workforces or offshore operations
- Strong project coordination skills: ability to juggle multiple workstreams and priorities simultaneously
- Clear, professional written communication for both internal stakeholders and workforce teams
- Familiarity with data labeling concepts, annotation workflows, and quality assurance processes
- Comfort working with cloud platforms or willingness to learn
Preferred:
- Hands-on experience with AWS Ground Truth or similar labeling platforms (Scale AI, Labelbox, etc.)
- Background in computer vision, media monitoring, or sports/sponsorship analytics
- Experience working with Eastern European teams or across time zones
- Understanding of AI/ML pipelines and where human review fits within them
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