• Led the creation and maintenance of data pipelines for large datasets, ensuring accuracy, reliability, and timeliness for US government clients.• Developed and validated machine learning models, including logistic regression and random forests, for fraud detection, reducing case investigation caseloads by 10%.• Built advanced dashboards to visualize relationships between millions of patients and prescribers, leveraging tools such as SAS Visual Analytics to identify prescribers of interest.• Explored, Cleaned, and analyzed unstructured data using NLP methods such as sentiment analysis and topic detection to be inputs for clustering and community detection for identifying persons of interest.• Created custom analytics visualizations and dashboards using SAS Visual Analytics for exploring Object Detection Models, Community Detection model results, and NLP output like sentiment and topic.• Led project modeling Covid spread through nursing homes using Python libraries like Pandas and NumPy for data manipulation and models like random forest and XGBoost for analysis. Presented findings using data visualizations generated by Matplotlib and Seaborn libraries.• Automated and maintained ETL processes for over 7 million records using PostgreSQL and REST APIs, facilitating fraud detection and cluster analysis efforts.• Collaborated with clients to understand their data needs and develop custom solutions that enhanced data-driven decision-making.• Developed machine learning pipelines for imputation of missing hospital data using SAS and SQL, ensuring the accuracy and completeness of critical healthcare datasets.• Used clustering methods to enhance model accuracy by grouping hospitals based on geographic and demographic data.

Ingest data from a variety of sources such as DFS and PostgreSQL, create an ETL process, produce data visualization reports, and offer analytic insight for customers. Working in Windows and Linux environments leveraging tools such as SAS programming, SAS Visual Analytics, python, REST API's, and SAS Visual Investigator.

Webscrape and clean data using R and Excel from user profiles to best match them with available housing options.

Assisted professor to create new introductory statistics course which focuses on exploring and analyzing real world data in an interactive format through R Shiny apps. Also taught structured weekly labs on entry level statistics concepts such as t-tests, linear regression, and experiment design.

Collaborated with a team to process day-to-day accounting procedures, expense allocation, and timely reporting. I also reconciled general ledger accounts, performed monthly adjusting entries, and forecasted expense budgets

Compiled overviews of competitors and their products including financial reports and recent acquisitions to determine whether or not a company should be bought out or competed against