Data Scientist Talent Network

• 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