Reviewing ML challenges and determining whether they are well designed and technically solvable
Evaluating whether datasets contain meaningful and learnable signals
Identifying unintended shortcuts or artifacts in synthetic datasets
Determining whether tasks require genuine diagnosis of the underlying ML problem
Reviewing evaluation metrics and improvement thresholds
Detecting metric gaming, data leakage, and evaluation flaws

🎯 Requirements

3+ years of hands-on applied machine learning experience
Strong experience with: ML experiment design, Model selection, Hyperparameter tuning, Model
Strong understanding of train, validation, and test splits
Ability to identify: Data leakage, Label noise, Distribution shift, Spurious correlations, Feature
Experience evaluating whether performance improvements are statistically meaningful rather than
Strong understanding of ML evaluation metrics and when different metrics are appropriate

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