• Saved the International team over $500K by building an hourly currency exchange rate web scraper that loads data to Postgres, where it is modeled with P&L data in a view for Tableau.• Strategized the proposed the AWS architecture for the Human Resources Data Lake, including the ETL from our data sources to an outside HR Analytics vendor, as well as developing PII data masking proofs of concept in AWS Glue and S3.• Built numerous proofs of concept in the AWS Cloud using Glue, Lambda, S3, Secrets Manager, and Redshift and created documentation and standards for our Data Engineering Center of Excellence (COE). • Worked with our AWS Consulting Partner to develop the infrastructure and environments, including proper access control, to begin developing BI and Data Science projects in the AWS Cloud.• Provided guidance and feedback on Tableau dashboards being transitioned to Power BI to lower costs, including initial approval before UAT.• Led the technical transfer of over 20 operationalized data analytics projects to an offshore team and saved the BI team hundreds of thousands of dollars.• Provided leadership, stability, and business knowledge to the BI team after senior leadership departed and assumed responsibility for operationalized analytics projects.

• Saved Reynolds $500K by rapidly building a Python ETL from a COVID-19 vaccination registration form to Postgres, creating a view that harmonizes vaccine and HR data, and using Google’s Tesseract-OCR software to validate submitted vaccination cards. • Brought visibility of aluminum health risk consumer sentiment to the Marketing team by using Python to scrape articles, using Natural Language Processing and a Random Forest Classifier to filter relevant articles, and building a custom sentiment analyzer.• Saved the Quality team days of time per month by building a Python Azure App that pulls consumer response data from the Microsoft Dynamics CRM API and loads it to Postgres, where it is modeled in a view for Tableau.• Streamlined the Quality Team’s manufacturer review process by using Python to extract quality test Excel workbooks from SharePoint, clean and transform them, and model them in a Postgres view that calculates KPI's.• Used Postgres functions to build a custom three-level invoice spend taxonomy for the Procurement team that enabled them to analyze spend by multiple categories.

• Debugged and maintained a consultant-built Python ETL for the Waste Bag Bulk Roll Master Data, which extracted data from numerous sources, including Excel, APIs, SQL Server, web scrapers, and IoT devices.• Improved data quality by rearchitecting and rebuilding the ETL and data model for the Waste Bag Coefficient of Friction Optimization Machine Learning project.• Convinced the Digital Manufacturing team to abandon their linear regression solution to a Coefficient of Friction project and to develop a more sophisticated regression model that would be compatible with a linear programming (PuLP) optimizer.• Enabled Human Resources to do workforce data analytics by developing a Python ETL that cleans, transforms, and integrates dozens of Excel workbooks and calculates headcount, terminations, retention, turnover, overtime, and DE&I KPI’s for Tableau.• Provided visibility to contract manufacturing spend by business unit, product category, country, and manufacturer for the Global Commercialization team by using Python and a PostgreSQL view to create a custom taxonomy for invoice data.

• Saved hundreds of hours of company time per week by using SQL, Python, Natural Language Processing, and parallel processing to text mine Panjiva import data for competitive intelligence related to Reynolds product categories.• Updated Reynolds daily on the ongoing tariff and trade war by using Python and Natural Language Processing to scrape, summarize, and categorize news articles and email an HTML newsletter.• Saved the International team dozens of hours of data analysis time per month by building a Python ETL that cleans and transforms their P&L and Annual Operating Plan data and loads it to Postgres, where it is modeled in a view for Tableau.

∗ Critically evaluated alternative explanatory hypotheses about human rationality. ∗ Synthesized new research and developed a novel theory of epistemic rationality. ∗ Taught symbolic logic, inductive logic, and critical thinking to undergraduates. ∗ Presented technical papers at conferences with 25-100 participants.∗ Met regularly with colleagues to discuss and provide feedback on their research.