Sidney J Reynaud, Jr

Data Analyst | Power BI Developer | Data Scientist

Welcome To My Portfolio

Sidney J Reynaud, Jr.

Hey everyone, I'm Sidney. With over a decade in lab operations and clinical analytics, I build reproducible, stakeholder-ready solutions that bridge technical depth with human relevance.

My portfolio explores predictive modeling, survival analysis, and disease trajectories across breast, prostate, and colon cancer, FSGS kidney disease, bipolar disorder, schizophrenia, and autism spectrum development. I work hands-on with Python, AWS, SHAP, LIME, Tableau, Power BI, and Looker Studio—delivering cloud-based ETL pipelines, interpretable models, and visual storytelling with version control and transparency.

Some of this research is still ongoing, and every project reflects a commitment to clarity, ethics, and actionable insight—because healthcare data deserves nothing less.

My Projects

Project One Preview

🎯 Predictive Analytics: Breast Cancer Survival

This project applies machine learning and clinical data science to model survival outcomes in breast cancer patients. Using predictive algorithms, SHAP/LIME interpretability, it uncovers key risk factors, stratifies patient profiles, and delivers stakeholder-ready visualizations to support precision oncology and early intervention.

Documentation View Dashboard
Project Two Preview

🔬 FSGS Kidney Disease Analytics

This project will explore Focal Segmental Glomerulosclerosis (FSGS) through a clinical data science lens—leveraging predictive modeling, SHAP/LIME interpretability and visualize disease progression. The goal is to deliver actionable insights for nephrology stakeholders while maintaining privacy safeguards and visual clarity across all outputs.

Documentation View Dashboard
Project Three Preview

🧬 Prostate Cancer Survival Modeling

This project will explore Focal Segmental Glomerulosclerosis (FSGS) through a clinical data science lens—leveraging predictive modeling, SHAP/LIME interpretability and visualize disease progression. The goal is to deliver actionable insights for nephrology stakeholders while maintaining privacy safeguards and visual clarity across all outputs.

Documentation View Dashboard
Project Four Preview

🧬 Colon Cancer Survival Analytics

This project will apply clinical data science to model survival outcomes in colon cancer patients. Using predictive algorithms, SHAP/LIME interpretability, it will uncover key risk factors, stratify patient subtypes, and deliver stakeholder-ready visualizations to support precision oncology and early intervention strategies.

Documentation View Dashboard

🧠 Technical Skillset

📊 Data Visualization & Storytelling

  • Power BI
  • Tableau
  • Looker Studio
  • Excel Dashboards

🔍 Data Analysis & Exploration

  • Python (Pandas, NumPy, Matplotlib, Seaborn)
  • SQL
  • Advanced Excel
  • Google Sheets

🧬 Predictive Modeling & Interpretability

  • Scikit-learn, XGBoost
  • SHAP & LIME
  • Survival Analysis
  • Privacy Safeguards

🧬 🏗️ Data Modeling & ETL Engineering

  • Power Query (M)
  • DAX
  • Star Schema Design
  • ETL Processes

☁️ Cloud Technologies & Infrastructure

  • AWS Cloud Technologies
  • GitHub
  • Jupyter Notebooks

Send me a message

Interested in collaborating or have questions about my work? I'd love to hear from you!