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Data Scientist Talent Network (Train AI Models Part Time!)

MercorUnited States🇺🇸United StatesPosted Sep 30, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
United States
SQLMachine LearningPython

Job Description

hackajob is collaborating with Mercor to connect them with exceptional professionals for this role.

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

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