Data Science at Mercor
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

17 Nov, 25

Salary

60.0

Posted On

17 Aug, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Computer Science, Mathematics, Statistics, Statistical Modeling, Data Science

Industry

Information Technology/IT

Description

IDEAL QUALIFICATIONS

  • Bachelor’s, Master’s, or PhD degree in a quantitative field such as data science, computer science, statistics, or mathematics OR 2+ years of industry experience in a data science role
  • Proficiency in Python data science libraries (e.g., pandas, numpy, scikit-learn).
  • Solid understanding of data analysis techniques, statistical modeling, or machine learning principles. Experience in identifying patterns, trends, and associations within complex datasets, as well as visualizing results to communicate insights, is highly valued.
  • Excellent analytical and data-driven writing skills. The ability to distill complex data-driven insights into clear, concise, and compelling analyses is crucial.
Responsibilities

ROLE OVERVIEW

Mercor is hiring highly skilled Data Science experts to join a significant research collaboration with one of the world’s leading AI labs. The role involves contributing to the development of advanced AI agents by creating evaluations for exploratory data analysis. You’ll help train, test, and calibrate these AI systems through identifying patterns, trends and associations in datasets and visualizing the results using Python.

KEY RESPONSIBILITIES

  • Evaluate data analysis produced by AI systems for quality and accuracy
  • Understand dataset context and execute statistical analysis & modeling for both vague and specific prompts
  • Design prompts and create detailed rubrics for fine-grained reward modeling and evaluation.
  • Produce gold-standard responses including data visualizations, explanatory text, and executable Python code (ipynb file).
  • Clearly translate data scientist reasoning, decision-making, and expertise into gradable criterion for AI agents
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