● Automated purchase order decision making with custom Python package, deployed on AzureDatabricks. Package reads and processes data using PySpark, performs a series of calculations usingPandas and NumPy, then the output is presented in Power BI● Used Sklearn regression to estimate the percent of each product being lost during the mixing andpackaging processes● Lead reporting initiatives for Procurement, Maintenance, and Sustainability groups. Automated dataprocessing and visualization using Power BI

•Responsible for revenue and expense forecasting for all segments of a budget worth almost $3.5billion• Automated the reporting of the revenue and expense forecasts using R and Excel• Used ARIMA models to forecast the cash flows of several funds in R

• Automated volume analysis and trade strategy identification reports using Tableau, Python, and SAP Business Objects• Pulled and validated data from the SAP database, cleaned and processed data using Python, generated reports and visualization with Tableau, and deployed auto-updating reports with Tableau Server• Responsible for monthly client-facing sector summary reports, created with Business Objects for analysis and Excel for visualization• Conducted exploratory analysis into the validity of a potential product

• Automated outlier detection for asset class pricing. Program detects when a price in the company’s database doesn’t accurately reflect the market• Used OneTick, Python (specifically pandas), and VBA• Attended weekly lunch and learns on different asset classes and company operations with respect to those classes

• Working for Professor Luca Grandi on the Xenon1t experiment• Coded in Python to do data analysis for the single photo-electron acceptance of the detector