Human Data Manager, New Grad

Rate not published

Workload: ~15h/week
Engagement: Project-based
Location: Worldwide
Field: Other

Manage human-data workflows used to train AI systems, from annotation and collection through validation, analysis, and reporting. This remote, full-time new-graduate role emphasizes data quality, operational efficiency, KPI design, and cross-functional execution.

What the work involves

You would design and manage workflows for collecting, annotating, processing, validating, and analyzing human data used in AI training. Day-to-day work may include monitoring quality and operational KPIs, investigating workflow bottlenecks, maintaining reporting frameworks, presenting recommendations, and refining processes as data volume or project needs change.

What the platform is likely to screen for

The screening will look for concrete evidence from coursework, internships, research, or projects that you can work accurately with data and improve an operational process. Expect follow-up questions about how you defined metrics, validated data, diagnosed errors, chose among competing improvements, and communicated findings to collaborators. AI experience is not required. Experience with Excel, Tableau, Power BI, Python, or SQL is preferred, as is project or internship work involving data management, AI/ML, process optimization, or human data.

Practical details

Remote and full-time
Hiring is taking place across multiple regions for coverage, compliance, and time-zone alignment
Responsibilities are described as consistent across regions, while employment terms vary
Exact working hours, time-zone requirements, and the balance between synchronous and asynchronous work are not stated

What they look for

Project Management
Data Operations
Quality Analyst
Human Data

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether you can give verifiable details about your ownership, the dataset, the workflow, and the resulting output rather than describing a team project only at a high level.
A strong answer: Identify the project context, data size and source, your exact responsibilities, the tools or methods used, and a measurable result or concrete deliverable. Be ready to distinguish your work from that of teammates.
Where people lose it: Candidates lose points by saying they cleaned or analyzed data without explaining what was wrong with it, what steps they performed, or how they verified the final dataset.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.