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
Listing sourced from Mercor. Annotation Academy is independent of these platforms and does not guarantee work or pay. See our disclosures.