* GenAI use case lead* Developed RAG + fine tuning for ICD code extraction from clinical charts for HCC risk adjustment* Architected and implemented Azure Databricks MLOps object oriented framework for efficient and robust AI/ML development

End-to-end AI/ML architect and data science practice leader for improving Kaiser Permanente pharmacy workflows and clinical outcomes.* On-boarded, managed and mentored three data scientists, leading them to deploy multiple production AI/ML models.* Leveraged A/B testing and causal machine learning to build personalized multi-modal outreach targeting systems that optimized national CMS Five-Star medication adherence and mail order utilization outcomes.* Developed foundational forecasting suites (Pytorch, gradient boosting and statistical methods) for Rx cost and volume, including over 20,000 different Rx's at multiple levels of geographic aggregation for strategic pharmacy staffing, inventory optimization and budgeting.* Built LLM solution (Mixtral finetune) for creating structured data from free-text Rx sigs, facilitating patient safety and medication reconciliation workflows* Architected KP Pharmacy's first AI/ML stack (MLOps, CI/CD and data pipelines) within on-prem requirements.* Won 2023 KP National Data Science Competition: Predicting anti-psychotic medication adherence

AI/ML SME and end-to-end developer for clinical and operational workflows and strategic planning.* Developed Pytorch DeepAR forecasting model for predicting daily patient emergency room and urgent care volumes to assist with COVID-19 surge staffing.* Built a regional cardiac services ensemble forecast driven scenario analysis tool which guided SCAL cardiac catheterization, electrophysiology and cardiovascular surgery infrastructure planning; won 'Most Innovative' project at 2020 KP ML Conference* Implemented a glassboxed Catboost regressor for predicting physician EMR messaging burden to drive SCPMG practice efficiency improvement.* Created LightGBM surgical case length prediction model and user frontend for at Baldwin Park Medical center to improve operating room scheduling accuracy.* Built daily, year ahead inpatient pediatric census forecast with prediction interval-based decision metrics to identify periods of high and low risk for over and underutilization

AI/ML and business intelligence support for Southern California Kaiser Permanente's clinical quality analytics team.* Lead statistician for JAMA OPEN publication involving the development of two LightGBM O/E risk adjustment models, which revealed addressable variance in admission and length of stay outcomes in KP SCAL NICUs.* Developed parametric distribution-based LOS goal target-setting methodology that allowed for apples to-apples comparisons across heterogeneous metrics and observation units.* Created custom control charts based on BCa confidence intervals for robust process improvement monitoring of highly skewed metrics.* Built multiple Tableau dashboards using complex data pipelines from the electronic medical record and other data sources, including the Hospital Quality Composite which is used for tracking inpatient care quality across the SCAL region.* Initiated team's first code review and git version control workflows.

Predictive and prescriptive AI/ML decision support for university administration