Labeling Analyst Entry Level Contract

Company Intelligence

Limited details available as the employer is unnamed and listed via Indeed as a generic contract posting. Likely a mid-sized firm in data annotation, AI/ML, or quality assurance industry, given the role's focus on interpreting acceptance criteria for labeling tasks—common in tech/services sectors outsourcing entry-level data work. No specific history, size, news, culture, values, mission, or office/hybrid policies identified in postings; contract nature suggests remote/US-based flexibility with minimal on-site requirements.

Program Deep Dive

This is a 1-year entry-level contract as Labeling Analyst, running through March 2026 with possible extension.

• Structure/Timeline: Full-time contract starting soon (implied urgency from 2026 focus); no formal program cohorts or rotations mentioned—straight to operational tasks.
• Skills/Competencies: Ability to correctly interpret high-level acceptance criteria; strong attention to detail, data entry accuracy, and analytical reading essential. No degree specified, suiting 18-25 recent grads/students.
• Daily Responsibilities: Analyze and label data per criteria (e.g., AI training datasets, quality checks); likely repetitive review of content like text/images for compliance. Learning: Build precision in data handling, exposure to acceptance standards.
• Mentorship/Training: Not detailed; expect basic onboarding for criteria interpretation.
• Career Progression: Extension possible; transitions to permanent data analyst or QA roles in similar firms, leveraging contract experience for AI/data entry portfolios.

Application Success Guide

• Requirements/Deadlines: No formal education/experience listed—entry-level open to students/recent grads. Apply via Indeed link; no deadline stated, but 2026 timeline implies apply ASAP before March end.
• Step-by-Step Process:

1. Search "entry-level-2026-jobs" on Indeed and locate Labeling Analyst.
2. Submit resume/cover letter highlighting detail-oriented tasks (e.g., school projects, part-time data work).
3. Complete any Indeed one-click apply; follow up via employer contact if provided.

• Common Interview Questions: Expect behavioral: "Describe a time you ensured accuracy in repetitive tasks," "How do you interpret complex instructions?" Technical: Walk through sample criteria interpretation.
• Assessments/Case Studies: Likely short test on labeling sample data or criteria review—no centers mentioned.
• Standout Candidate: Demonstrate precision via examples (e.g., proofreading, inventory); quantify accuracy (e.g., "95% error-free in group project").

Insider Tips

• Interview Priorities: Value attention to detail over tech skills; show soft skills like reliability via punctuality/examples. Practice verbal criteria explanation.
• Technical vs Soft Skills: Soft skills (organization, communication) prioritized for entry-level; basic Excel/data entry a plus.
• Industry Knowledge: Reference data labeling in AI/ML (e.g., training models via annotated datasets); mention tools like Labelbox if familiar.
• Questions to Ask: "What are common extension criteria?" "How does this role support team projects?" Shows interest in growth.
• Red Flags to Avoid: Typos in application (ironic for labeling role); vague resume examples; lateness. Don't overclaim experience—honesty key for contracts.

Practical Information

• Salary/Stipend: $30-$32/hour (approx. $60K-$65K annualized full-time).
• Benefits: 401(k), health/vision/dental insurance (pulled from full description—confirm upon apply).
• Start Dates/Duration: Immediate start implied; 1-year through March 2026, extendable.
• Networking/Alumni: Indeed platform for similar roles; build LinkedIn with "Labeling Analyst" keyword for data/AI contractors. No formal alumni noted—leverage for freelance data gigs post-contract. Actionable Advice: Tailor resume to "acceptance criteria" keyword; apply today via Indeed link as postings fill fast. Use this contract to gain AI data experience—hot for 2026 entry-level tech shifts. Track application in spreadsheet for follow-ups.