Data Validation Specialist

Anderson Merchandisers

About The Position

The Data Validation Specialist checks that data entering and moving through company systems is accurate, complete, and consistent. The role labels, tags, categorizes, and reviews large volumes of structured and unstructured data—including text, images, audio, video, and multimodal content—according to detailed project guidelines. The specialist also validates work completed by other annotators, identifies inconsistencies or errors, and provides feedback to improve annotation quality and guideline clarity. The role requires meticulous, detail-oriented work, precise adherence to complex and evolving instructions, and the ability to flag ambiguity rather than guess. Hours scale up during peak periods, such as new AI model launches, and down when workload is lower. Assignments are checked for progress through a reporting mechanism by the Artificial Intelligence Project Manager. The position involves computer-based work, mostly at a desk. Weekly hours vary based on workload; schedule notice is provided at least one week ahead where possible. Some overlap with business hours (8:00 a.m.–5:00 p.m. CST) is required, with occasional evening and weekend hours.

Requirements

High school diploma or equivalent.
Demonstrated ability to follow detailed written instructions precisely.
Strong attention to detail and consistency, especially during repetitive tasks performed over long periods.
Comfort working with ambiguity and ability to flag uncertain cases rather than guess.
Basic computer literacy, including comfort using web-based tools, spreadsheets such as Excel, Google Sheets, or Microsoft List, and ticketing or 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

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 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 peer or vendor annotations for accuracy, completeness, and adherence to guidelines.
Identify systematic errors, edge cases, and ambiguous instructions, and escalate patterns to team leads or guideline owners.
Perform spot-checks and full audits on annotated datasets before delivery or model training.
Track and report quality metrics, including accuracy, error, agreement, and throughput, using internal tools or spreadsheets.
Re-annotate or correct flagged data points and document the rationale for changes.
Provide structured feedback on annotation guidelines, identifying gaps, contradictions, and 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.
Use 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 for sensitive, proprietary, or personally identifiable data.
Promptly communicate blockers, ambiguous cases, or technical issues to team leads.