Data Validation Specialist

Anderson Merchandisers

1d•Remote

About The Position

The Data Validation Specialist checks that data entering and moving through our systems is accurate, complete, and consistent. The Data Validation Specialist is responsible for labeling, tagging, categorizing, and reviewing large volumes of structured and unstructured data (text, images, audio, video, or multimodal content) according to detailed project guidelines. This role also validates work completed by other annotators, identifies inconsistencies or errors, and provides feedback to improve annotation quality and guideline clarity. The ideal candidate is meticulous, comfortable with repetitive, detail-oriented work, able to follow complex and evolving instructions precisely, and confident flagging ambiguity rather than guessing. As a flex position, the role scales hours up during peak periods such as new AI model launches and down when volume is lower. Assignments are checked to verify progress, through a reporting mechanism by the Artificial Intelligence Project Manager.

Requirements

High school diploma or equivalent required
Demonstrated ability to follow detailed written instructions with a high degree of precision.
Strong attention to detail and consistency, especially across repetitive tasks performed over long periods.
Comfortable working with ambiguity, and skilled at flagging uncertain cases rather than guessing.
Basic computer literacy: comfortable navigating web-based tools, spreadsheets (Excel/Google Sheets/Microsoft List), and ticketing/task systems.
Strong written communication skills for documenting edge cases and providing feedback.
Ability to meet productivity and quality targets in a metrics-driven environment.

Nice To Haves

Preferred prior experience in data annotation, content moderation, transcription, quality assurance, proofreading, editing, or a related detail-oriented field.

Responsibilities

Label, tag, transcribe, segment, or categorize data (text, images, video, audio, or sensor data) according to project-specific style guides and taxonomies.
Apply bounding boxes, polygons, keypoints, or segmentation masks to visual data as required by computer vision projects.
Maintain consistent labeling speed while meeting or exceeding minimum throughput targets without sacrificing accuracy
Review annotations completed by peers or vendor teams for accuracy, completeness, and adherence to guidelines.
Identify systematic errors, edge cases, and ambiguous instructions; escalate patterns to team leads or guideline owners.
Perform spot-checks and full audits on annotated datasets prior to delivery or model training.
Track and report quality metrics (accuracy rate, error rate, agreement rate, throughput) using internal tools or spreadsheets.
Re-annotate or correct flagged data points and document rationale for changes.
Provide structured feedback on annotation guidelines, identifying gaps, contradictions, or unclear edge cases.
Participate in calibration sessions to align understanding of labeling standards across the team.
Help pilot new annotation tasks, test draft guidelines on sample data, and report findings before full-scale rollout.
Document recurring edge cases and contribute examples to internal knowledge bases or FAQs.
Work within annotation platforms to complete assigned tasks.
Track task assignments, deadlines, and completion status in project management tools.
Maintain strict confidentiality and data handling protocols, especially with sensitive, proprietary, or personally identifiable data.
Communicate blockers, ambiguous cases, or technical issues promptly to team leads.