Job Openings Data Collection Lead

About the job Data Collection Lead

Key Responsibilities


1. Data Collection Project Management

  • Manage assigned data collection projects from initiation through final delivery.
  • Understand client requirements and translate them into structured collection plans.
  • Define project scope, timelines, milestones, resource requirements, and execution plans.
  • Coordinate all activities required to launch and operate data collection projects.
  • Track project progress, dependencies, risks, costs, and delivery commitments.
  • Maintain project documentation and provide regular progress visibility to relevant stakeholders.
  • Proactively identify bottlenecks and take corrective actions to keep projects on track.

2. Collection Methodology & Project Setup

  • Design practical data collection methodologies based on project requirements.
  • Determine how, where, and through whom the required data should be collected.
  • Define participant profiles, demographic requirements, geographic coverage, collection environments, devices, equipment, and other project-specific criteria.
  • Develop clear collection guidelines and instructions for resources and partners.
  • Define acceptance criteria and validation requirements before collection begins.
  • Set up appropriate workflows for submission, tracking, validation, rejection, and recollection.
  • Conduct pilots where necessary to validate the methodology before scaling production.

3. Freelancer, Vendor & Resource Coordination

  • Identify and engage suitable freelancers, field resources, agencies, communities, or vendors for assigned data collection projects.
  • Coordinate with existing vendor and recruitment networks to source resources based on project requirements.
  • Evaluate potential resources and partners based on capability, geographic coverage, availability, quality, cost, and reliability.
  • Brief vendors and freelance resources thoroughly on project requirements and collection guidelines.
  • Monitor resource availability and collection capacity throughout the project.
  • Maintain strong working relationships with external resources and partners.
  • Address resource-related issues quickly to avoid disruption to project delivery.

4. Data Quality & Validation

  • Establish quality control processes appropriate for each collection project.
  • Ensure collected data meets client-defined technical, demographic, geographic, and quality specifications.
  • Work with QA, Data Analytics, and other relevant teams to establish validation workflows.
  • Monitor incoming data and identify recurring quality issues early.
  • Conduct root-cause analysis where collection quality falls below expectations.
  • Provide corrective guidance to freelancers, vendors, and collection teams.
  • Manage rejection and recollection processes where required.
  • Ensure quality remains consistent as collection volumes scale.

5. Pilot & Scale-Up Management

  • Design and execute pilot collections before full-scale production where required.
  • Validate collection instructions, resource capability, submission workflows, and quality standards during the pilot stage.
  • Identify gaps and refine the collection methodology based on pilot results.
  • Develop scale-up plans once the collection approach has been validated.
  • Ensure sufficient resources and operational capacity are available before scaling.
  • Maintain quality and process discipline as collection volumes increase.

6. Client & Stakeholder Coordination

  • Communicate directly with clients regarding data collection requirements, operational challenges, edge cases, and requests for clarification.
  • Serve as the primary operational point of contact for client questions related to collection methodology, feasibility, quality, resources, and execution.
  • Proactively raise collection-related risks, ambiguities, deviations, and constraints with clients.
  • Seek timely clarification from clients when requirements, examples, acceptance criteria, or edge cases are unclear.
  • Explain the operational impact of client requirements and recommend practical solutions or alternatives.
  • Communicate project progress, collection findings, quality trends, and corrective actions directly to clients as appropriate.
  • Translate client feedback and decisions into clear operational actions for internal teams, freelancers, vendors, and collection resources.
  • Ensure client expectations remain aligned throughout the collection lifecycle.
  • Keep relevant internal stakeholders informed of important client communications, decisions, and changes.

7. Cost & Operational Management

  • Support costing and budgeting for data collection projects.
  • Obtain and evaluate rates from freelancers, vendors, and collection partners.
  • Monitor project costs against approved budgets.
  • Identify efficient sourcing and execution models without compromising data quality.
  • Track resource productivity and collection efficiency.
  • Support commercial discussions by providing realistic operational assumptions and cost inputs.

8. Data Collection Compliance & Documentation

  • Ensure collection activities follow project-specific requirements related to consent, privacy, confidentiality, and data handling.
  • Ensure participant consent and required documentation are properly collected where applicable.
  • Maintain traceability of collected data and relevant metadata.
  • Ensure freelancers and vendors follow required security and confidentiality protocols.
  • Escalate potential compliance or data-rights concerns to appropriate stakeholders.

9. Process Development & Continuous Improvement

  • Document data collection methodologies, workflows, and lessons learned from completed projects.
  • Develop reusable SOPs, templates, checklists, and collection frameworks.
  • Identify recurring operational challenges and implement improvements.
  • Build institutional knowledge around different types of data collection projects.
  • Help strengthen Quantigo AI's overall ability to scope, plan, and execute increasingly complex data collection requirements.
    
    Who You Are
    Core Competencies
    • Strong project management and execution capabilities.
    • Practical understanding of AI data collection operations.
    • Strong resource and vendor coordination skills.
    • Excellent problem-solving and troubleshooting ability.
    • Strong attention to data quality and project specifications.
    • Comfortable managing distributed freelancers and external partners.
    • Able to turn ambiguous requirements into structured execution plans.
    • Strong client-facing communication and stakeholder management skills.
    • Comfortable discussing operational challenges, edge cases, and requirements directly with clients.
    • Comfortable operating under tight timelines and changing project requirements.
    • High ownership and accountability for project outcomes.

    Qualifications & Experience
    • Bachelor's degree in Business, Operations, Technology, Computer Science, or a related field.
    • 3–5 years of experience in project management, data operations, or related roles, with direct exposure to AI data collection projects.
    • Hands-on experience managing data collection projects from planning through delivery.
    • Experience communicating directly with clients about project requirements, operational challenges, quality issues, edge cases, and clarifications.
    • Experience coordinating freelancers, distributed workforces, field resources, or external vendors.
    • Strong understanding of data collection workflows, quality validation, resource planning, and project tracking.
    • Experience working with international or geographically distributed data collection projects is preferred.
    • Familiarity with AI/ML data requirements and Human-in-the-Loop operations is strongly preferred.
    • Experience with project management and data tracking tools is preferred.

    Preferred Project Exposure
    Experience managing one or more of the following types of data collection projects would be highly valuable:
    • Image and video collection
    • Speech and audio recording
    • Multilingual data collection
    • Text and conversational data
    • Computer Vision datasets
    • Human-generated AI training data
    • LLM and Generative AI datasets
    • Multimodal data collection
    • Geographic or field-based collection
    • Demographic-specific data collection
    • Domain expert / SME data collection
    • Mobile device or sensor-based collection

    Why Join Quantigo AI
    At Quantigo AI, you will have the opportunity to manage diverse data collection programs supporting AI companies across different domains, geographies, and data modalities.
    This role provides significant ownership over how data collection projects are designed and executed. You will work directly with clients as well as internal teams, freelancers, vendors, and global stakeholders while helping Quantigo AI strengthen its data collection capabilities and take on increasingly complex AI data requirements.

    Position Details
    Industry: Artificial Intelligence, Machine Learning, Data Services
    Position Type: Full-Time (Permanent)
    Location: Global
    Work Mode: Remote
    Other Benefits: As per company policy



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