About the role

Role Overview

Join HiredBuddy as a Junior Data Analyst on a 100% remote team in the United States. You’ll help improve the quality and performance of AI training datasets by monitoring data quality, analyzing annotator metrics, optimizing labeling pipelines, and communicating operational insights.

Responsibilities

Data Quality Assurance: Audit, clean, and validate large datasets to ensure precision and compliance with annotation guidelines.
Performance & Metrics Tracking: Analyze annotator accuracy, turnaround times, and throughput using SQL, Python, and BI dashboards.
Pipeline Optimization: Partner with Operations, AI Specialists, and Product to identify bottlenecks in labeling pipelines and propose data-driven improvements.
Reporting & Dashboards: Build and maintain daily/weekly operational reports and dashboards for internal and client stakeholders.
Guideline Calibration: Study edge cases and error patterns to refine annotation instructions, rubrics, and project guidance.

Requirements

Education: Bachelor’s degree in Data Analytics, Computer Science, Statistics, Information Systems, or related quantitative field (or equivalent experience).
SQL: Strong proficiency for querying, aggregation, and analysis.
Python or R: Hands-on data manipulation experience (e.g., Pandas, NumPy).
Visualization Tools: Experience with Tableau, Power BI, or Looker.
Spreadsheets: Advanced Excel / Google Sheets skills (Pivot tables, VLOOKUP/XLOOKUP, complex formulas).
Analytical mindset: Strong problem-solving and attention to detail.
Communication: Translate findings into actionable insights for non-technical stakeholders.
Work authorization: Must be legally authorized to work in the United States.

Nice-to-Haves

Familiarity with AI/ML, data annotation workflows, or RLHF.
Prior operations-focused analyst experience or internships.

About HiredBuddy

HiredBuddy connects domain specialists and subject-matter experts with leading AI research labs to shape and train the next generation of AI. The company works on high-quality data annotation, including RLHF (Reinforcement Learning from Human Feedback) and model evaluation across multiple domains such as STEM, software engineering, linguistics, and data science.