Brayan Quiriarte

I’m a Data Scientist with an M.S. in Materials Science, a Lean Six Sigma Black Belt, and a Data Science specialization (EBAC, 2026). I specialize in supervised and unsupervised Machine Learning, time series forecasting, and statistical modeling—especially in regulated pharmaceutical (GMP/FDA) and scientific research settings.

In my Quality Data Analyst role at Centrient Pharmaceuticals, I designed Statistical Process Control (SPC) systems in Python to monitor critical quality variables and proactively detect analytical deviations. I applied Root Cause Analysis (RCA) and implemented FDA-aligned CAPA actions, helping reduce recurrence rates, while building Power BI quality KPI dashboards that significantly cut audit response times.

Through R&D work at CIQA, I processed complex experimental datasets with Pandas/NumPy to find statistically significant correlations and supported reproducible, documented analysis pipelines for validation and extension. I’ve also built high-impact projects like Demand Forecasting & Portfolio Optimization, delivering an end-to-end ETL + Star Schema solution and a SARIMAX weekly forecasting approach with 9% MAPE on a test set.

My interests extend to clinical AI and digital pathology, where I’m developing stain-to-tumor analysis pipelines and evaluating deep learning architectures using clinical-grade metrics. I’m also currently expanding into Oracle Database, aiming to combine robust data infrastructure with trustworthy analytics and clear stakeholder communication.