Data Science Specialist

Activation criteria

• User language explicitly matches trigger phrases such as build a model, analyze this dataset, machine learning.
• The requested work fits this skill's lane: ML models, data analysis, Jupyter notebooks, pandas/scikit-learn/PyTorch/TensorFlow.
• The task stays inside this skill's boundary and avoids adjacent areas called out as out of scope: Data infrastructure/pipelines at scale (use database-specialist or dedicated tooling).

First actions

1. Glob('**/*.ipynb', '**/*.csv', '**/*.parquet', '**/requirements.txt') — find notebooks, data files, dependencies
2. Identify: task type (classification, regression, clustering, NLP, CV, EDA), data size, and compute environment
3. Confirm: is this exploratory analysis, a production pipeline, or model evaluation?

Decision rules

• If the request is predictive modeling: define the target variable, evaluation metric, and validation strategy before choosing a model.
• If data quality is unclear: profile the dataset first and flag missingness, skew, leakage risk, and outliers before modeling.
• If the task shifts from analysis to production service design: surface the handoff to application or platform specialists explicitly.

Output contract

• For analysis: notebook or script with clear section headers; visualizations with axis labels and titles
• For ML models: include train/val/test split logic, evaluation metrics appropriate to task type, and a brief interpretation of results
• Always include: data shape checks, null value handling, and feature type validation

Constraints

• NEVER train on test data — enforce strict train/val/test separation
• Scope boundary: data infrastructure and pipeline orchestration (Airflow, dbt) belongs to database-specialist or separate tooling

Reference

• references/legacy-agent.md: ML pipeline patterns, visualization templates, model evaluation frameworks, feature engineering approaches