• Compiled, defined, and meticulously analyzed data requirements for various healthcare projects, resulting in the creation of over 15 comprehensive data reports to presented these reports to stakeholders.• Developed & implemented ML algorithms to accurately predicted health insurance claims, improving forecast accuracy by 20%.• Leveraged Natural Language Processing (NLP) techniques to enhance documentation procedures, successfully converting over 5,000 hours of spoken content into accurate text records, streamlining workflows and improving 30% documentation efficiency.• Utilized Snowflake data platform to conduct advanced analysis, transforming complex raw data sets into clear, actionable insights.• Conducted rigorous statistical analysis and modeling to uncover trends, patterns, and correlations within large data sets, facilitating data-driven decision-making processes that improved patient outcomes and optimized resource allocation.• Designed and executed robust cross-validation techniques, including k-fold and stratified k-fold, alongside hold-out schemes to assess the significance of predictive models.• Performed comprehensive risk analysis of project requirements to pinpoint critical success factors, leading to the strategic prioritization of functional requirements.• Collaborated closely with team members to identify specific data needs, delivering tailored visualization & reporting solutions.

• Assisted in data collection, cleansing, and validation processes, ensuring accuracy and integrity of data.• Presented findings & insights derived from ML models to stakeholders, providing suggestions for process optimization & growth.• Creating dashboards in Power BI by utilizing measures, calculated columns, slicers, DAX calculations, groups, and hierarchies.• ed data pipelines using SQL and Apache Spark for efficient data extraction, transformation, loading (ETL) processes.• Applied SAS programming techniques to clean, transform, and analyze large datasets, ensuring data quality and integrity.• Utilized Apache Spark and Hadoop for big data processing, enabling efficient analysis of large-scale datasets.• Applied data warehousing principles, including the design and implementation of fact tables, dimensional tables, and dimensional data modeling techniques such as star schema and snowflake schema.• Implemented visualization using Seaborn, Matplotlib, Plotly and Pandas, NumPy to perform exploratory data analysis.

• Utilized Python and SQL to extract, clean, and manipulate data from various sources, ensuring data quality and integrity.• Implemented data validation and cleansing techniques to ensure data accuracy and reliability before loading into the Data Warehouse, Performed Data Mining to analyze the patterns of the data.• Contributed to the development of ML models by utilizing frameworks such as TensorFlow, Keras, PyTorch, and scikit-learn.• Developed and deployed enterprise-grade Power BI solutions, resulting in an 80% increase in user adoption and satisfaction.• Utilized DAX and Power Query for complex data transformation and calculations.• Built good partnerships with stakeholders, providing frontline support for business needs, achieving 60% user satisfaction improvement.