Data Analyst with experience in healthcare and business analytics, specializing in data analysis, visualization, predictive modeling, ETL pipelines, and dashboard development. Holds an M.Sc. in Data Science from the Univ…
Data Analyst with experience in healthcare and business analytics, specializing in data analysis, visualization, predictive modeling, ETL pipelines, and dashboard development. Holds an M.Sc. in Data Science from the University at Buffalo – The State University of New York and a B.Tech. in Computer Science from KL University.
Conducted meticulous analysis across 110+ healthcare data sources, revealing trends, patterns, and critical insights for strategic decision-making.
Designed and implemented a Tableau dashboard for remote patient monitoring, integrating vital sign tracking and visualizing key performance metrics (KPIs).
Contributed to a 40% reduction in in-person visits.
Collaborated with cross-functional teams, translating business needs into actionable technical tasks and projects.
Executed the creation and implementation of predictive models, including XG Boost, Random Forest, Neural Networks, and Logistic Regression, to identify patient risk factors and forecast adverse events.
Engineered an AWS S3-based ETL pipeline for patient journey trigger analysis, employing Python and SQL scripts to enhance data efficiency and scalability.
Leveraged SQL to manipulate and analyze diverse healthcare datasets from various sources, extracting information for optimizing operational processes and enhancing data-driven strategies.
As a seasoned data analyst in the healthcare sector, I conducted a comprehensive analysis across 110+ data sources, extracting valuable trends and critical insights to guide strategic decision-making. Notably, I designed and implemented an impactful Tableau dashboard for remote monitoring, leading to a remarkable 40% reduction in in-person visits by efficiently tracking vital signs and patient conditions. Collaborating seamlessly with cross-functional teams, I translated complex business needs into actionable technical tasks and projects. My role extended to executing the creation and implementation of predictive models, accurately identifying patient risk factors and forecasting adverse events to enhance overall healthcare effectiveness. Leveraging the ggplot2 tool for data visualizations, I reduced data pre-processing time by 28%, streamlining analysis tasks for heightened efficiency. I also played a key role in establishing and overseeing vital performance metrics (KPIs) to monitor and analyze patients' health status. Moreover, I engineered an AWS S3-based ETL pipeline for patient journey trigger analysis, deploying Python and SQL scripts to elevate data efficiency and scalability.
Developed machine learning models using Python and SAS to analyze client behavior, resulting in a 45% enhancement of Q1 2021 retention rates.
Leveraged data visualization tools to present monthly sales trends, optimizing resource allocation and achieving a 20% reduction in overhead costs.
Spearheaded the inaugural A/B test for the sales page, contributing to a 7.5% increase in customer conversions.
Utilized the K-means clustering algorithm for customer segmentation based on behavior and preferences, resulting in targeted marketing strategies and a 25% improvement in customer engagement.
Worked with internal stakeholders to create and manage Power BI reports and dashboards, monitoring customer retention, sales, operations, and inventory.
Demonstrated expertise in Data Analysis Expressions (DAX), creating customized calculations using PowerPivot for Microsoft Excel workbooks and Analysis Services tabular model projects.
Led comprehensive analysis of 25,000 monthly users, guiding strategic marketing and product decisions.
Achieved a threefold increase in average engagement time and a 30% reduction in drop-off rates.
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