Expert Talent Pool, Data Operations (Expert, 5+ Years)
remotecontract
Remote
mid
$94k–$187k
H1B: no
Clera Expert Talent Pool, Data Operations Expert leads data operations and quality assurance for AI model training datasets. Manage data sourcing, annotation guidelines, and coach annotators to ensure dataset quality.
Responsibilities
Lead data operations for teams developing advanced AI models, owning the quality of datasets used for training and evaluation. This flexible expert network matches experienced data operations professionals with project work based on their backgrounds.
Lead data sourcing and collection programs by writing specifications, setting acceptance criteria, and tracking yield and quality.
Manage media capture and document contribution at scale, then curate and validate the resulting datasets.
Design synthetic data generation pipelines and validation checks to assess data quality.
Review annotations, adjudicate disagreements, and run calibration and audit rounds with annotator teams.
Write and maintain labelling guidelines, identifying gaps and incorporating new edge cases.
Coach annotators and reviewers, and report agreement rates, error rates, and other quality metrics to project teams.
Qualification
Experience with document contributionExperience with dataset curationExperience coaching annotators
Required
At least 5 years of experience in data operations, annotation QA, data quality, dataset management, or a relevant domain with hands-on human-data work.
Experience leading or reviewing others' work as a QA lead, guideline owner, adjudicator, team lead, or data project manager.
Experience running data sourcing or collection programs, including setting specifications, acceptance criteria, and quality measures.
Proficiency with annotation tools such as Label Studio, Labelbox, CVAT, Scale, Remotasks, or Appen, and spreadsheets or SQL for quality analysis.
Experience owning labelling guidelines and conducting annotation reviews, calibration, or audits while measuring agreement or error rates.
Clear written communication for explaining data quality issues and solutions. Experience with synthetic data validation, large-scale media capture, document contribution, dataset curation, or coaching annotators is also relevant.
