Data Scientist for AI System Evaluation

Role Overview

Shape evaluation tasks for AI systems used in enterprise data and analytics environments. This remote opportunity focuses on scenarios reflecting the data scale, model complexity, and business-critical decision-making found in large public companies.

Key Responsibilities

• Create enterprise data science scenarios covering large-scale predictive modeling, analytics governance involving multiple stakeholders, and complex data infrastructure decisions.
• Develop tasks across machine learning development, enterprise data pipelines, business intelligence at scale, experimentation and causal inference, and data strategy.
• Design data and MLOps scenarios using tools such as Snowflake, Databricks, Python or R, SQL, Tableau or Power BI, and enterprise ML platforms including SageMaker, Vertex AI, and MLflow.
• Apply statistical rigor, A/B testing frameworks, model validation, and MLOps practices to produce reference analyses, model documentation, and executive-level insights.
• Write rubrics that distinguish sound enterprise data science judgment from generic textbook or tutorial-level responses.

Qualifications

• At least 5 years of experience as a data scientist, analytics leader, or machine learning engineer within a Fortune 500 technology or enterprise organization, or within a Fortune 500 data or analytics organization.
• Direct ownership of enterprise data products, analytics initiatives, or production machine learning systems.
• Fluency in enterprise data science tools and methodologies, with practical knowledge of data governance, privacy compliance, and cross-functional stakeholder alignment.
• Experience authoring rubrics, technical curriculum, or model documentation is preferred.

Work Terms

• Remote, hourly engagement.

Compensation

• $60 to $70 per hour.