About Me
Applied Data Scientist with a Ph.D. and 6+ years of experience in real-world data (RWD) analytics, predictive modeling, and causal inference in healthcare and behavioral science. I design reproducible, FAIR-compliant data pipelines for large-scale longitudinal and observational studies, generating real-world evidence (RWE) for clinical and operational decision-making. Skilled in Bayesian modeling, mixed-effects models, and machine learning applied to patient-reported outcome measures (PROMs), risk stratification, and outcomes research. Strong record of cross-functional collaboration, peer-reviewed publication, and translating complex data into actionable healthcare insights.
Technical Toolkit: Python (scikit-learn, pandas, numpy, PyTorch) | R (brms, lme4, tidyverse, Shiny) | SQL | Git/GitHub | CI/CD | Docker | Azure | Machine Learning | Bayesian Modeling | Mixed-Effects Models | Causal Inference | Survival Analysis | A/B Testing | NLP | PROMs Analysis | FAIR Compliance | DataLad | REDCap | LLMs | Reproducible Research Workflows
Experience
Senior Research Analyst | Cumming School of Medicine, University of Calgary | 2024 - Present
Data Scientist | Bee Touch | 2023 - 2025
Assistant Professor | Ambrose University | 2022 - 2025
Horizon Postdoctoral Researcher | Concordia University | 2020 - 2021
Education
Federal University of São Carlos (UFSCar) & University of Tenessee, Knoxville (UTK) | Ph.D. in Psychology | 2016 - 2020
HarvardX | Professional Certificate - Data Science | 2021 - 2022
Stanford | Specialization - AI in Healthcare | 2025 - Present
