Analytics Portfolio

Professional Projects

Given the nature of my professional experience, much of my work cannot be shared publicly. For example, at Cardlytics I worked directly with the largest banks in the US, analyzing more than half of all US credit and debit transactions. That is certainly a sensitive data set! I am beyond grateful for that unique opportunity. What’s more, I am proud my clients and colleagues took great care to protect the privacy of their customers. I am working on a more robust sample and I will update this page often with new projects.

In the meantime, you can find a sample of my Cardlytics work in their Research and Insights and Purchase Intelligence pages. In addition to my client work, I contributed to projects including Holiday Spend analyses and the State of Spend report launched in response to the COVID-19 pandemic.

For a sample of my work at Mather, check out their case studies page. Please note that I did not write these case studies myself. However, many of them reference my work directly or projects comparable to my own. My work included Circulation Budget Forecasting and Paywall & Engagement testing for Publishers, Prize Structure and Payout Optimization for Lotteries, among many others.

Much of my work at Rightpoint was as a leader to a rapidly growing team. My team and colleagues built new & better experiences for our clients across a variety of industries. Our work spanned Customer, Employee, and Product experiences, which you can read about here.

Data Visualizations

Here you can find some reports and dashboards that I’ve built in my spare time. Many of these are ongoing works in progress and are likely to change often.

Computer Vision Exploration

After completing my Masters in Analytics at Georgia Tech, I was fascinated by computer vision and the possibilities it presented. I started experimenting with it in 2021 for things like facial recognition, object detection, and making silly filters.

In my own experimentation, I tried to focus on methods that work without relying too heavily on massive third party datasets. In particular, I wanted to explore approaches less reliant on the enormous amounts of physical resources and behind-the-scenes human labor involved in building those datasets and the associated models. I believe it is important to acknowledge the unseen costs of these models, both as a practical matter and an ethical one. I found Margaret’s Mitchell’s Pillars of a Rights-Based Approach to AI Development to be a compelling and thoughtful framework.