Remote AI Data Collection Project Lead

Pampa Talents; Argentina; On-site; ARS 1,200,000 - 2,400,000; Full time

Benefits offered by this job

100% Remote (LATAM residents)

Full-Time Position (EST hours)

U.S. Holidays Off

Job summary

Pampa Talents in Argentina seeks a Project Manager Data Collection for AI to structure, lead, and secure data collection projects for AI use cases. The role sits at the interface between business needs, technical teams, and external partners.

You will define objectives, scope, volumes, languages, formats, deadlines, budget and quality criteria; set up workflows for collection, annotation, validation and delivery; coordinate internal and external resources; monitor risk and ensure data quality

Qualifications

• Experience in data collection, annotation, or dataset management.
• Ability to translate AI data needs into actionable plans.
• Strong organization and quality orientation.

Responsibilities

• Define data collection projects: objectives, scope, volumes, languages, formats, deadlines, budget, and quality criteria
• Set up workflows for data collection, annotation, validation, and delivery.
• Coordinate internal and external resources involved in the projects.
• Monitor project progress and anticipate operational risks
• Ensure the quality of the data produced and their conformity to specifications
• Ensure clear reporting to internal teams and management
• Participate in structuring the methods, tools, and best practices of data collection for AI.

Skills

Data collection

Project management

Quality control

Stakeholder coordination

Multilingual communication

Job description

Pampa Talents in Argentina seeks a Project Manager Data Collection for AI to structure, lead, and secure data collection projects for AI use cases. The role sits at the interface between business needs, technical teams, and external partners.

You will define objectives, scope, volumes, languages, formats, deadlines, budget and quality criteria; set up workflows for collection, annotation, validation and delivery; coordinate internal and external resources; monitor risk and ensure data quality