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Data Science Expert - Fully Remote

Remote / Online - Candidates ideally in
San Francisco, San Francisco County, California, 94199, USA
Listing for: mercor
Remote/Work from Home position
Listed on 2026-10-12
Job specializations:
  • IT/Tech
    Data Analyst, Machine Learning/ ML Engineer, Data Science Manager, Data Engineering
Salary/Wage Range or Industry Benchmark: 70 USD Hourly USD 70.00 HOUR
Job Description & How to Apply Below
Position: Data Science Expert - Fully Remote | Upto $70/hr

About the job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey . Position:
Data Science and Analytics Experts Type:
Contract Compensation: $60–$70/hour

Location:

Remote

Role Responsibilities
  • Construct enterprise data science scenarios for large-scale predictive modeling and multi-stakeholder analytics governance at Fortune 500 accounts.
  • Build analytics tasks across machine learning model development , enterprise data pipelines , and business intelligence at scale.
  • Develop data and MLOps scenarios using tools like Snowflake , Databricks , Python/R , SQL , Tableau/Power BI , and enterprise ML platforms such as Sage Maker , Vertex AI , and MLflow .
  • Apply enterprise data science methodologies, including statistical rigor , A/B testing frameworks , and MLOps best practices to produce reference analyses and executive-level insights.
  • Author rubrics that distinguish authentic enterprise data science judgment from generic textbook recall.
Qualifications - Must-Have
  • 5+ years working as a data scientist, analytics leader, or ML engineer at a Fortune 500 technology or enterprise organization.
  • Direct ownership of F500 data products , analytics initiatives , or machine learning systems in production.
  • Fluency in enterprise data science tooling and methodologies, with an understanding of F500 data governance and privacy compliance.
Preferred
  • Prior rubric, technical curriculum, or model documentation authorship.
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