Cell & Molecular Biologist | Scientific Data Analytics & Automation
I develop practical scientific and quality systems analytics tools for laboratory, assay, and QMS workflows, with a focus on Python, reproducible analysis, data visualization, risk-based decision support, and process improvement.
Scientific Data Analysis & Automation
Wolf Analytics is my independent scientific analytics and automation portfolio, focused on applying computational tools to practical life science, laboratory, and quality systems problems.
Python-based QMS analytics workflow using 100% synthetic data to evaluate quality event aging, CAPA effectiveness, change management performance, root cause trends, risk, organizational capability, and management review priorities.
The project includes reproducible synthetic data generation, automated technical QC, independent human plausibility review, KPI reconciliation, an interactive Streamlit dashboard, and a formatted management review report.
➡️ View the QMS Performance & Change Management Analytics project
Python-based analysis of synthetic 96-well ELISA data incorporating 4PL curve fitting, assay QC, visualization, precision, recovery, dilution correction, and scientific data interpretation.
Automated Python/OpenCV analysis of 96-well ELISpot plate images with plate and well detection, spot quantitation, QC metrics, heatmaps, batch processing, and synthetic ground-truth validation.
Scientific Data Analysis · Quality Systems Analytics · QMS · CAPA · Change Management · Root Cause Analysis · Risk Prioritization · Immunoassays · ELISA · ELISpot · Assay QC · Python · Automation · Data Visualization · Reproducible Analysis
Portfolio projects use synthetic data and contain no client, employer, proprietary, confidential, patient, or unpublished experimental data.