Data Scientist Talent Network

Summary

Mercor is building a talent network of experienced data scientists for potential future projects evaluating how effectively AI systems perform real-world data science work, including designing grading criteria and assessing ML/statistical deliverables.

Mercor is building a network of experienced data scientists for potential future projects with leading AI research organizations. These projects may focus on evaluating how effectively AI systems perform real-world data science work.

There is no immediate project opening, but qualified applicants may be contacted as relevant opportunities become available.

2. Potential Responsibilities

Future projects may involve:

  • Designing precise, task-specific grading criteria for data science deliverables, including exploratory data analyses, statistical modeling work, machine learning pipelines, experimentation and A/B test write-ups, feature engineering, and technical reports or notebooks

  • Evaluating AI-generated or human-created work against established criteria

  • Providing detailed written justifications for evaluations and scores

  • Applying consistent, evidence-based judgment so that assessments are reproducible and defensible

  • Incorporating structured feedback from senior reviewers and iterating on submitted work

Specific responsibilities will vary depending on the project.

3. Ideal Qualifications

  • 1+ years of professional data science experience

  • Experience at a leading technology, research, or quantitative firm (such as top FAANG, AI labs, top-tier quant funds, or equivalent)

  • Strong command of Python, SQL, statistical modeling, machine learning, experimentation and causal inference, and translating messy real-world data into rigorous analyses

  • Exceptional written communication skills, including the ability to convey technical findings clearly

  • A detail-oriented and consistent approach to evaluating complex work

  • Comfort receiving feedback and calibrating judgment against established standards

See also

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