As a Data Analyst, I automated workflow assessments and document reviews using Python and SQL, reducing manual effort by nearly threefold and enabling data-driven process improvements. I engineered end-to-end ETL pipelines leveraging Python, SQL, and Azure Data Factory, and developed Tableau dashboards integrated with document data to streamline reporting and analysis. By implementing machine learning models using Scikit-learn, TensorFlow, and Statsmodels for forecasting and root cause analysis, I reduced process errors by 25%. Additionally, I enhanced data quality and accessibility through rigorous cleaning, validation, and integration in SQL and Azure Databricks, significantly improving the reliability and speed of insights. I also optimized over 70 client surveys and 40 business assessments using Power BI and Excel, further minimizing manual effort and accelerating data-driven decision-making.

I used Pandas and NumPy to clean and transform big datasets, which improved the quality of modeling inputs and reduced data inconsistencies by 32%. Using scikit-learn, I developed and refined machine learning models that enabled predictive insights to assist business choices and achieved up to 87% accuracy in categorization tests. In order to identify important trends, I also carried out exploratory data analysis (EDA) and produced visualizations, which enhanced reporting effectiveness and directed strategic activities.