Human Data Manager

Job Summary

The Human Data Manager coordinates, evaluates, and optimizes high-volume human data annotation pipelines to deliver pristine validation training datasets to frontier AI models. Core daily tasks include designing step-by-step annotation guidelines, analyzing dataset metrics to identify production blockages, managing key performance indicators (KPIs) for expert cohorts, building visual data reporting dashboards, and collaborating across technical divisions to adjust operational pipelines in line with shifting client objectives.

Required Education & Graduate Status

• Academic Profile: Recent Bachelor's or Master's degree graduate (Graduated within the last 0–24 months) in Computer Science, Data Analytics, Data Science, Industrial Engineering, Operations Management, Systems Engineering, Economics, Finance, or a closely related, quantitative analytical field.

Required Experience & Technical Competencies

• Data Aptitude: Proven academic or project-based exposure to core data analysis concepts, structured data validation practices, or digital workflow optimization models.
• Metrics & Analytics Translation: Demonstrated capability to interpret abstract, complex performance datasets and translate findings into clear, persuasive verbal and written optimization strategies.
• Performance & Execution Drive: A strong, visible drive to learn quickly, perform efficiently under short turnaround timelines, and maintain strict attention to detail within a highly competitive, remote environment.
• Communications Masterwork: Exceptional written and verbal communication skills in English, possessing the baseline confidence needed to direct cross-functional alignment conversations.

Preferred Technical Qualifications

• Engineering Focus: Academic concentration in an engineering or highly technical field accompanied by a direct focus on human-in-the-loop (HITL) processes or data annotation initiatives.
• Analytics Tool Stack Fluency: Practical familiarity with modern data visualization platforms (such as Microsoft Excel, Tableau, or Power BI) or basic programming/querying languages (such as Python and SQL).
• Portfolio Validation: Prior completion of a technical internship, intensive capstone project, or open-source contribution targeting process optimization, database hygiene, or AI/ML training operations.

Key Responsibilities

1. Robust Workflow Architecture & Annotation Management (30%)

• Engineer Data Pipelines: Design, construct, and manage end-to-end data annotation workflows that ensure the accurate collection, labeling, processing, and parsing of diverse data streams.
• Coordinate Expert Teams: Act as the primary operational coordinator for global networks of domain experts, ensuring clear assignment distribution and guideline compliance.
• Prevent Quality Deficiencies: Build multi-tiered verification loops to intercept and resolve data formatting errors, missing metadata, and human labeling variances before final export.
• Author Operational Guidelines: Draft comprehensive, step-by-step documentation, annotation rubrics, and compliance frameworks to guide technical annotators through complex domain tasks.

2. Operational Analytics & Performance Optimization (25%)

• Monitor Operational KPIs: Establish, measure, and track daily operational key performance indicators (KPIs) to analyze worker accuracy, output capacity, and processing speeds.
• Identify Workflow Bottlenecks: Run diagnostic data reviews across active collection pipelines to isolate structural delays and implement swift process adjustments.
• Generate Actionable Insights: Uncover hidden operational patterns across historical data logs, generating data-driven recommendations to improve overall training dataset efficiency.
• Optimize Process Scalability: Continuously adjust internal tooling setups to support massive dataset expansions while preserving absolute data integrity.

3. Reporting Framework Design & Dashboard Management (25%)

• Build Reporting Infrastructure: Create and maintain comprehensive dashboard frameworks that display system-wide data quality trends for executive review.
• Connect Cross-Project Data: Aggregate data metrics from separate workflow channels into unified spreadsheets, sorting results by time zone, expert domain, and error type.
• Run Data Queries: Utilize advanced Excel configurations or SQL scripts to query deep data warehouses, keeping reporting layers consistently updated.
• Present Operational Updates: Author clear, concise weekly summary briefs that outline workflow improvements, capacity milestones, and pipeline bottlenecks.

4. Cross-Functional Collaboration & Regional Compliance (20%)

• Connect Technical Teams: Act as the primary operational link connecting client product needs with micro1's internal technology groups and globally distributed expert networks.
• Drive Project Alignment: Partner closely with Product Managers and Machine Learning Scientists to update active data collection strategies as target model metrics change.
• Manage Global Compliance: Coordinate across parallel international time zones, ensuring localized workflow tracking matches data security laws and cross-border data protection policies.
• Practice Extreme Ownership: Maintain a proactive, solution-first approach to operations, independently identifying systemic system bugs and resolving them before they disrupt client deadlines.

Core Competencies & Skills

• High-Velocity Learning: Exceptional ability to rapidly study and master highly technical terms and specialized terminology across unfamiliar subject areas.
• Structural Problem-Solving: A structured engineering approach to operational management that breaks down complex workflow bottlenecks into clear, logical correction steps.
• Meticulous Data Quality Control: A highly focused testing mentality that systematically reviews text and data entries to ensure perfect conformity with design guidelines.
• Flexible Intercultural Collaboration: Strong emotional intelligence and communication skills, comfortable interacting smoothly with diverse international workforces across asynchronous networks.
• Resilient Stress Management: Maintaining high organization, logical thought, and communication clarity when running high-volume data campaigns with strict deadlines.

Skills Required:

• Computer / Software / It / Data
• Economics / Statistics
• Industrial / Manufacturing