Data Collection Lead, AI/ML Data Programs

Whitecollars

. Manage assigned data collection projects from initiation through final delivery .

Posted 10/16/2026full-timeRemote • Brazil, India, Bangladesh, South AfricaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing data collection projects, translating client requirements into actionable plans, and ensuring quality control throughout the data lifecycle. Proficient in coordinating resources, optimizing workflows, and enhancing AI data operations across various modalities.

Highest-signal resume keywords

Project ManagementData OperationsAI Data CollectionClient CommunicationProcess Improvement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Collection ManagementQuality Control WorkflowsRoot-Cause AnalysisHuman-in-the-Loop OperationsMultimodal Data ExperienceComputer VisionLLM and Generative AI ProjectsImage and Video CollectionSpeech and Audio RecordingVendor Evaluation

Soft Skills

Stakeholder ManagementResourcefulnessStructured ExecutionProactive Risk ManagementClient Communication

Certifications & Qualifications

Bachelor's Degree in BusinessBachelor's Degree in OperationsBachelor's Degree in TechnologyBachelor's Degree in Computer Science

Industry Keywords

AI Data OperationsData ModalitiesDistributed TeamsFreelancer CoordinationOperational Gaps

About the role

Key responsibilities & impact

Manage assigned data collection projects from initiation through final delivery
Translate client requirements into structured plans, timelines, milestones, and execution workflows
Design collection strategies defining participant profiles, geographic coverage, devices, environments, metadata requirements, and quality criteria
Run pilot collections, assess results, identify operational gaps, and refine methodologies before production scaling
Identify, evaluate, and coordinate freelancers, field resources, communities, agencies, and external vendors
Plan resources and capacity, monitor performance, and adjust resource allocation
Establish quality-control workflows and validate collected data against client specifications
Identify recurring data issues and manage corrective actions, rejections, and recollection
Communicate with clients about requirements, progress, risks, feasibility, and operational challenges
Monitor commitments, dependencies, costs, and delivery risks; resolve bottlenecks and escalate concerns
Document methodologies and build reusable SOPs, templates, and checklists
Continuously improve collection workflows and strengthen AI data operations across domains, geographies, and data modalities

Requirements

What you’ll need

3–5 years of experience in project management, data operations, or a related field
Direct exposure to egocentric AI data collection projects
Experience with image and video collection, speech and audio recording, multilingual datasets, computer vision, multimodal data, or LLM and generative AI projects
Experience with Human-in-the-Loop operations and AI/ML data requirements is a significant advantage
Experience coordinating distributed teams and external resources
Experience evaluating vendors and mobilizing resources quickly
Ability to balance quality, cost, timelines, and feasibility
Strong client communication and stakeholder management skills
Ability to clarify requirements, explain implications, and recommend workable alternatives
Root-cause analysis and process improvement skills
Resourcefulness, structured execution under pressure, and proactive risk management
Bachelor's degree in Business, Operations, Technology, Computer Science, or a related field

Benefits

Comp & perks

End-to-end ownership of data collection projects
Exposure to diverse AI programs, data modalities, industries, geographies, and collection challenges
Direct impact on AI development through high-quality datasets
Opportunity to develop reusable methodologies and improve operational workflows
Cross-functional collaboration with clients, internal teams, freelancers, and external vendors
Professional growth in project leadership, distributed resource management, data quality, and AI/ML operations
Fully remote work flexibility
Collaboration with a globally distributed team