United States
● Accomplished Data Analyst with over 3 years of experience in driving insights and operational efficiencies through advanced analytics and machine learning. ● Proficient in Python, R, SQL, SAS, and MATLAB, with extensive experience in cloud and big data technologies such as AWS, GCP, and Snowflake. ● Skilled in exploratory data analysis, data visualization, and dashboard development using Tableau and Power BI. ● Demonstrated expertise in conducting thorough exploratory data analysis (EDA) to uncover valuable trends, anomalies, and patterns. ● Successfully increased predictive accuracy by 20% through advanced data analysis and machine learning modeling. ● Improved database performance by 25% by streamlining data storage and retrieval processes. ● Demonstrated expertise in data mining, cleansing, statistical analysis, ETL, and data warehousing, and adept at implementing scalable solutions and efficiently handling large datasets. ● Utilizes methodologies such as SDLC, Agile, and Waterfall to deliver data-driven results. Holds a Master's degree in Data Science from the University of Houston.
• Designed and implemented highly scalable data pipelines using Python , achieving a significant reduction in data processing time. These pipelines were instrumental in enabling real-time analytics on critical medical claims data, showcasing expertise in optimizing data workflows for efficiency and real-time insights. • Developed and implemented ETL processes to streamline data ingestion from diverse sources including claims databases, member records, and healthcare providers' data, ensuring accuracy and timeliness of data for analysis and reporting purposes. • Created compelling visualizations with PowerBI, including Cross Tabs, Heat Maps, Box and Whisker Charts, Scatter Plots, Geographic Maps, Pie Charts, Bar Charts, and Density Charts to uncover actionable insights. Proficient in data visualization and dashboard creation to present complex data insights clearly and effectively to stakeholders. • Utilized advanced SQL queries and Mongo DB to extract actionable insights from large-scale healthcare datasets, facilitating strategic decision-making for product development and risk assessment. • Engineered data architecture solutions , leveraging services to optimize storage and processing costs, decreasing data processing time while ensuring scalability and reliability. • Demonstrated ability to work with large datasets and perform exploratory data analysis (EDA) using R and Python to identify trends and patterns. Conducted data mining, data wrangling, and statistical analysis for insights extraction, applying SDLC, Agile, and Waterfall methodologies.
● Improved sentiment analysis accuracy by 20% using advanced Python libraries, enhancing our ability to understand customer feedback and tailor strategies more effectively. ● Conducted thorough data analysis using SQL Queries in MySQL across various project phases, extracting valuable insights that informed strategic decision-making and project direction. ● Performed advanced data analysis, including predictive modeling, clustering, and time-series forecasting, to identify trends and patterns that informed business strategies. ● Implemented A/B testing with advanced statistical techniques like regression analysis, resulting in significant improvements of 25% higher user engagement and 30% better system performance. ● Created and maintained interactive dashboards in Tableau facilitating clear communication of complex data findings to stakeholders. ● Leveraged advanced Excel functionalities such as Power Query and data modeling to automate data transformation tasks, resulting in a 40% reduction in manual processing time and increased reporting accuracy. ● Collaborated closely with development, testing, and project management teams to define KPIs and establish metrics, ensuring alignment with project goals and organizational objectives. ● Conducted detailed exploratory data analysis (EDA) to identify and resolve data anomalies, enhancing data accuracy by 25% and reducing processing time by 30%. ● Applied machine learning techniques using Scikit-learn to build predictive models, achieving a 25% increase in model accuracy and supporting data-driven decision-making. ● Managed ETL processes using S3 and Redshift to streamline data integration from multiple sources, reducing errors by 30% and ensuring data consistency and integrity throughout the project lifecycle. Environment: Python, SQL Queries, MySQL, SAP, Excel (Power Query, data modeling, pivot tables, VLOOKUP, macros), Tableau, Regression analysis, clustering, time-series forecasting, Scikit-learn, S3, Redshift