Michael Y.

💼 Python Statistical Model Validation Specialist | Forecasting, Backtesting & Drift Analysis I help businesses, researchers, and SaaS/fintech teams evaluate whether their forecasting, statistical, and machine learning models are reliable in real-world conditions. My work focuses on model validation, time-series forecasting, backtesting, data drift analysis, prediction diagnostics, and clear Python-based statistical reporting. I specialize in checking whether a model is not only accurate on paper, but also stable, well-tested, and trustworthy when data changes over time. ⭐ What I Can Help With ⭐ Forecasting model evaluation and improvement ⭐ Time-series analysis and backtesting ⭐ Data drift and distribution-shift detection ⭐ Prediction error analysis and model diagnostics ⭐ Statistical analysis using Python ⭐ ML model evaluation, calibration, and validation ⭐ Churn, retention, and SaaS analytics ⭐ Financial and business risk analytics ⭐ Scenario analysis and simulation ⭐ Clear technical reports with visualizations and recommendations 🛠 Tools & Technical Skills 🐍 Python: Pandas, NumPy, SciPy, Scikit-learn, Statsmodels 📈 Time-series analysis: forecasting, rolling validation, residual diagnostics 📊 Statistical modeling: regression, hypothesis testing, uncertainty analysis 🤖 Machine learning evaluation: calibration, error analysis, robustness checks 📉 Drift analysis: rolling metrics, distribution comparison, model stability checks 📑 Reporting: Jupyter Notebook, Excel, Power BI 🗄 Data handling: CSV, Excel, SQL basics 💪 My Strengths 💪 Strong statistical reasoning 💪 Out-of-sample testing and backtesting 💪 Model reliability and robustness checks 💪 Clean Python notebooks and reproducible workflows 💪 Clear explanation of technical results 💪 Research-level attention to detail 💪 Practical recommendations, not just charts 🎯 My Approach I combine statistical rigor with practical business understanding. My goal is to help clients answer important questions such as: • Is this forecasting model reliable? • Is the model overfitting? • Does performance break when the data changes? • Are the prediction errors acceptable? • Is there data drift or instability? • Can the results be trusted for business or research decisions? I deliver clean Python analysis, diagnostic plots, and clear reports that explain what is working, what is failing, and what should be improved next.