In my role within the Pharmacovigilance and HEOR departments, I harnessed SAS and R-programming to manage and analyze extensive datasets, improving data accuracy and model efficiency. My contributions included designing a macro for a large patient dataset (3 TB), reducing critical errors, and developing a Logistic Regression model with high predictive accuracy. I also employed PowerBI for dynamic data visualization, facilitating deeper insights into research findings and enhancing stakeholder reports. These efforts streamlined data processing, supported FDA approvals, and significantly contributed to predictive modeling and patient data analysis projects.

I spearheaded the creation of innovative ML applications for freelance clients, including facial and palm recognition systems. My technical prowess extended to developing a video conferencing application using WebRTC and ReactJS, enhanced with a basic authentication system and an ML-powered chatbot for handling user inquiries. Additionally, I shared my knowledge through tutoring and conducting interactive lab classes for university students, focusing on the data science domain, thereby nurturing the next generation of data scientists.

I developed a server automation system utilizing crontab and Python for scheduled tasks, enhancing server management through automatic email updates and remote access for administrators. I designed databases and maintained a Git repository for efficient version control, employing Tableau for data visualization and Python programming for data analysis and mining, significantly reducing processing time. My role also involved leading teamwork in code reviews, leveraging my data analysis expertise to streamline workflows, and implementing rigorous testing to identify and resolve potential bugs, demonstrating a strong blend of technical skills and collaborative teamwork.