Data Scientist Workflow Assistant

Problem to Solve

Data science work often stalls at concrete points: EDA stays at summary statistics without clarifying metric definitions, A/B tests focus on p-values while ignoring statistical power and business impact, and deployed models lack drift monitoring and reproducible documentation.
data-scientist-zh turns these steps into an engineering workflow: clarify the goal, constraints, and inputs, select statistical or machine-learning methods, then provide validation checks and actionable recommendations. It fits analysis and modeling tasks spanning pandas, scikit-learn, XGBoost, and PyTorch, as well as production-related work with SQL, PySpark, MLflow, and FastAPI.

How It Works

• Statistics and experiments: hypothesis testing, A/B testing, causal inference, time-series analysis, Bayesian modeling
• Machine learning: feature engineering, cross-validation, SHAP explainability, hyperparameter tuning
• Data engineering: data profiling, missing-value handling, data quality, feature stores, pipeline orchestration
• Delivery and communication: visualization, dashboards, executive reporting, reproducible code and method notes
  A typical run defines the business objective, explores the data, selects the right method, validates results, recommends next actions, and plans monitoring and maintenance.

Scope and Caveats

It focuses on data analysis and model workflows rather than replacing permissions, data access, or deployment environments. For compliance, production incidents, or financial risk work, regulatory, security, and governance constraints should be included in the task definition.