• Used data exploration to identify business opportunities for improvement: wrote time-series predictive modeling algorithm to predict jurisdictions with a growing customer-base which senior leadership used to drive strategic business decisions. • Gained heavy experience with all stages of the software and model development lifecycle: from querying large scale Oracle SQL databases to writing and refactoring documentation.• Collaborated with stakeholders to develop, design and test scalable machine learning solutions using complex Python scripts: - Became fluent in Pandas, Numpy, Catboost and Seaborn packages and experimented with PyTorch and TensorFlow. - Implemented a classification algorithm using categorical and highly skewed data.• Proficient in identifying and solving potential and existing application bottlenecks: refactored and improved existing python application runtime from 6 hours to 21 minutes.• Developed, maintained and productionized hands-on statistical solutions and machine learning models. These included batch (predicted switchers and stayers for Traditional Medicare vs Medicare Advantage) and real-time (continuously predicted incoming appeals) use cases. • Crafted and presented, knowledge-share presentations and analyses reports for immediate team and senior leadership on a regular basis. Subsequently wrote articles for the company-wide Enterprise Data Services newsletter, which went out to all 102,300 employees of Elevance Health

• Designed, developed, tested, documented and maintained a predictive classification machine learning model using Python.• Conducted large-scale exploratory data analyses of operational data to discern trends and delivered findings to business to help drive decision-making.• Wrote SQL queries to acquire datasets and create aggregate variables for analyses.• Used PowerBi to create interactive visualizations to details findings and predictions for business users.

• Utilized R to develop a program which generates desired cheese statistics by importing, manipulating and transforming data gathered by laboratory machines.• Developed linear regression algorithms to determine which production variables most influence the presence of coliform in cheese and which correlate most with profit yield.• Created cheese specification sheets by reading in, cleaning and analyzing data in R.

• Assumed responsibility of QA/QC materials and maintenance of laboratory equipment.• Analyzed air, production and raw material samples for coliform, mold and bacteria.• Analyzed trends in cheese acidity, salinity, and fat percentage to ensure quality products.