AUSTIN NUCKOLS

Collaborate with leadership in Marketing, CRM, Operations, Finance, and with Executive Leadership and play a role in driving strategic business decisions. Consulted regularly by the CFO and COO

Lead AB testing methodology for both Retail-based and customer-based tests. Key callout's:

• Designed, analyzed, and presented a successful retail price change experiment leading to company-wide roll-out with estimated incremental revenue lift of > $30M per year
• Designed a synthetic control experiment to analyze customer behavior following release of a new feature to the Loyalty program. Found no significant lift in customer spend behavior leading to rollback of the feature and ultimately saving the company an estimated $4M incremental profit
• Lead AB test analysis and iterative improvement of a New Customer Acquisition strategy with a 3rd party partner which has cumulatively led to >20k incremental new customers and >$25M incremental revenue

Employ advanced statistical techniques to pioneer new or enhance existing business capabilities. Key callout's:

• Applied linear regression to understand factors affecting the success of new store openings, identifying a key factor and leading to a strategic shift in marketing strategy for future store openings
• Applied negative binomial regression to model credit applications and analyze results of an AB test
• Applied Monte Carlo simulation in a power analysis to identify a range of possible outcomes and ultimately prevented a risky AB test
• Developed method for store clustering used to match stores up

Manage the Customer Analytics team (2 analysts, 1 data scientist) to ultimately drive value through analysis, prediction, and reporting:

• Lead the team on researching and developing key performance indicators to monitor business objectives including development of first "customer churn" metric at the company
• Automate and consolidate reporting and improve self-service capabilities
• Mentor the team on predictive modeling methodology and AB testing methodology resulting in a predictive model that predicts store opening sales with 20% MAPE

Developed ETL pipelines using Azure Data Factory and Databricks notebooks running Python, PySpark, and SQL

Automated analyses, generated dashboards or custom data products, and created predictive analyses including several notable projects:

• Exploratory Analysis of Quality Assurance data: found specific actions of client team were reducing internal team performance
• Time to Install Predictive Model Research: analyzed installation data and developed a preliminary predictive model for total installation time
• Time to Hire Predictive Model: Developed a Talent Acquisition Power BI dashboard with predictive model for estimating time to fill and presented to Executive Board

• Designed experiments for multiple projects, managed and analyzed data, and used results to drive future experiments
• Communicated findings through reports and visualizations, presentations, and manuscripts (three co-authored publications during my tenure)
• Led analyses on large protein datasets looking at thousands of proteins across disease and control samples and used inferential statistics to produce biological insight
• Empirically assessed Random Forest imputation accuracy on proteomics data to validate whether it could be used in that project

• Taught statistics and R programming as the teaching assistant for the graduate-level biostatistics course (Spring 2020 & Spring 2021)
• Developed and led an Intro to R and ggplot2 workshop (Nov 2020)
• Taught the gapminder R module of the Software Carpentry Workshop hosted by Emory Data Science for Scientists Club (Jan 2021 & Feb 2022)