Data Science Expert - AI Content Specialist

What You'll Do

• Craft rigorous data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more.
• Produce meticulous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answers.
• Evaluate outputs from libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practices.
• Identify logical failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to improve model thinking.
• Systematically record where and how AI models break down, contributing to the ongoing hardening of model reasoning across complex data science domains.

Who You Are

• Holds or is pursuing a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with heavy emphasis on data analysis.
• Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing.
• Highly detail-oriented — you catch subtle errors in code syntax, mathematical notation, and statistical conclusions.
• Self-directed and comfortable working independently on task-based assignments.
• No prior AI or annotation experience required.
• Nice to have: Experience with data annotation, data quality assessment, or evaluation systems; proficiency in production-level data science workflows (MLOps, CI/CD pipelines for models); familiarity with model evaluation frameworks or benchmark design.