Data Science and Analytics Experts

About this role

**Role Overview** - Mercor is seeking senior data science and analytics professionals to build evaluation tasks for AI systems operating in Fortune 500 enterprise data and analytics contexts. - The workflows are calibrated to the data scale, model complexity, and business-critical stakes of Fortune 500 and large public company data operations. - Contributors design enterprise data science scenarios, draft reference outputs, and write rubrics that capture how senior F500 data leaders think. **Key Responsibilities** - Construct enterprise data science scenarios spanning large-scale predictive modeling, multi-stakeholder analytics governance, and complex data infrastructure decisions at F500 accounts. - Build analytics tasks across F500 machine learning model development, enterprise data pipelines, business intelligence at scale, experimentation/causal inference, and data strategy. - Develop data and MLOps scenarios involving tools such as Snowflake, Databricks, Python/R, SQL, Tableau/Power BI, and enterprise ML platforms (SageMaker, Vertex AI, MLflow) in F500 stacks. - Apply enterprise data science methodologies (statistical rigor, A/B testing frameworks, model validation, MLOps best practices) and produce reference analyses, model documentation, and executive-level insights. - Author rubrics that distinguish authentic enterprise data science judgment from generic textbook or tutorial-level recall. **Ideal Qualifications** - 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization (Google, Meta, Amazon, Microsoft, Netflix) or inside an F500 data/analytics organization (JPMorgan, UPS, Unilever, PepsiCo, Walmart). - Direct ownership of F500 data products, F500 analytics initiatives, or F500 machine learning systems in production. - Fluency in enterprise data science tooling and methodologies, plus understanding of how F500 data governance, privacy compliance, and cross-functional stakeholder alignment actually work. - Prior rubric, technical curriculum, or model documentation authorship is a plus.

Skills & domains

• ai-training
• rlhf
• sme
• annotation
• Data Analysis