About this role
Shape evaluation tasks that reflect the scale, complexity, and business-critical decisions of enterprise data and analytics organizations. This remote, hourly role focuses on creating realistic scenarios, reference outputs, and assessment rubrics for AI systems working in large-company data environments.
Key Responsibilities- Create enterprise data science scenarios involving large-scale predictive modeling, analytics governance across multiple stakeholders, and complex data infrastructure decisions.
- Develop tasks covering machine learning model development, enterprise data pipelines, business intelligence at scale, experimentation and causal inference, and data strategy.
- Design data and MLOps scenarios using tools including Snowflake, Databricks, Python or R, SQL, Tableau or Power BI, and enterprise ML platforms such as SageMaker, Vertex AI, and MLflow.
- Apply statistical rigor, A/B testing frameworks, model validation, and MLOps best practices to produce reference analyses, model documentation, and executive-level insights.
- Write rubrics that differentiate sound enterprise data science judgment from generic textbook or tutorial-level responses.
- 5+ years of experience as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization, or within a Fortune 500 data and analytics organization.
- Experience directly owning enterprise data products, analytics initiatives, or production machine learning systems.
- Fluency with enterprise data science tools and methodologies, including practical knowledge of data governance, privacy compliance, and cross-functional stakeholder alignment.
- Experience authoring rubrics, technical curriculum, or model documentation is preferred.
- Remote, hourly engagement.
- $60 to $70 per hour.