I lead the design and implementation of scalable automation and intelligence systems that strengthen security, streamline operations, and enable smarter decision-making across the organization. This includes building an enterprise-wide offboarding framework supporting more than 1,800 employees, significantly improving compliance, access governance, and operational efficiency across critical systems.

Alongside this, I architected LLM-powered meeting intelligence pipelines that integrate with Microsoft Graph and AI transcription services to automatically generate structured records, sentiment insights, executive summaries, and follow-up actions for more than 100 client success meetings each day. I also modernized the internal task and project management architecture to improve cross-team visibility, execution tracking, and governance—helping teams move faster with clearer accountability and better operational insight.

I led enterprise data engineering and analytics initiatives focused on transforming fragmented clinical and operational data into scalable, intelligence-ready platforms. This included designing and deploying migration pipelines from Statwise and PointClickCare into Azure to support centralized reporting, advanced analytics, and machine-learning initiatives across multiple subsidiary organizations.

To strengthen data reliability and governance, I directed a comprehensive internal data audit spanning nine subsidiaries, significantly improving data integrity, standardization, and trust in downstream reporting. At the same time, I managed and mentored a cross-functional team of six data scientists and data engineers—establishing Agile and Kanban delivery frameworks, implementing CI/CD pipelines in Azure DevOps, and introducing scalable engineering best practices to support long-term growth.

Building on this foundation, I guided the development of a predictive machine-learning model that identified nurse attrition risk, enabling proactive workforce planning and targeted retention strategies using Python and Scikit-learn.

I developed immersive virtual tour and walkthrough experiences for site and laboratory environments using C#, enabling realistic spatial exploration and enhanced remote engagement. In parallel, I produced comprehensive technical documentation for AR headset deployment, utilization, and maintenance—streamlining device integration, improving troubleshooting efficiency, and increasing overall user self-sufficiency. I also led structured group user testing for VR-based training modules centered on laboratory simulations and procedural workflows, leveraging participant feedback to refine the learning experience and measurably improve user satisfaction.

I engineered automation-driven solutions to streamline academic data management and operational support, including developing a Python-based algorithm that consolidated diverse Excel datasets through automated merging and dynamic column mapping. I implemented web scraping processes to retrieve and maintain data from Drexel College of Computing & Informatics systems—eliminating an estimated 40–60 hours of manual effort—and built a Selenium-powered web application to bulk delete course shells, reducing processing time by approximately 30 minutes per course. In addition, I authored comprehensive technical documentation to strengthen knowledge transfer for future technical assistants and resolved complex technical issues, including previously undocumented problems, achieving a 94% resolution rate without escalation.

I enhanced the performance, reliability, and usability of a machine learning–driven data aggregation platform by redesigning the user interface and resolving critical data-loading issues, driving a 51% increase in internal site utilization. I led a strategic overhaul of the underlying data pipeline—introducing structured logging, unit testing, and object-oriented development practices while converting a complex R-based model into Python—resulting in a 21% reduction in execution time. To support secure and scalable operations, I leveraged AWS infrastructure and Bitbucket repositories for daily data storage, management, and retrieval. Throughout the project lifecycle, I communicated progress, technical insights, and performance outcomes to department leadership through monthly sprint review presentations.

I designed and implemented automation-driven workforce operations solutions that streamlined credential provisioning, onboarding oversight, and termination reporting. This included engineering automated credential creation workflows that significantly accelerated provisioning while reducing manual administrative effort, as well as building an onboarding monitoring application that saved approximately one hour and forty-five minutes of processing time each week. I also developed an automated monthly termination reporting solution that reduced reporting time from two hours to just twenty minutes, improving operational efficiency and enabling more timely workforce insights.

I architected and deployed a scalable AWS-based database platform to securely store and efficiently retrieve critical volunteer and donor information, establishing a reliable foundation for long-term data management. To enhance usability and adoption, I designed and developed an intuitive front-end interface that streamlined database interaction and improved accessibility for non-technical users. I also produced comprehensive technical documentation and instructional video guides to support onboarding, training, and sustained system utilization. In addition, I implemented automated shipping label generation and printing workflows for large user segments and engineered a dependable bulk data import pipeline to successfully migrate and integrate legacy datasets into the new environment.

Fellowship Consisting of Talmudic Law, Philosophy, and Ethical Leadership

Focused on advanced computer science concepts, specializing in machine learning, artificial intelligence.