Senior Data Scientist - F500 Expert
Listed on 2026-10-11
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IT/Tech
Data Analyst, Data Scientist, Data Engineering, Machine Learning/ ML Engineer
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.
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 (Sage Maker, 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.
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, Pepsi Co, 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.
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