Eva McGiff

- Conducted A/B tests to track the add-to-cart rate and determine the best possible layout of the site to drive customer purchases - Built and deployed collaborative filtering models to enhance product recommender systems - Scripted a Python Crawler for the US, UK, DE, IE Wayfair sites to detect unexpected changes onsite, incorrect information and differences between the front-end and back-end data - Calculated, tracked and visualised SEO KPIs (visits, impressions, exit/bounce rate, rank)

- Forecasted product demand using machine learning in Python to predict the number of necessary Locker locations. Facilitated growth by pinpointing the geographic areas with the largest customer need for additional Locker locations. - Facilitated strategic decisions by creating an automated scenario analysis which showed how new fleets of Locker locations to the existing fleet would impact coverage and reduce costs. - Optimised the Locker selection site through iterative A/B tests and Google Analytics.

- Created a Python application which replaced the existing Excel macro process to convert data into usable results for further analysis. Reduced process time by ~98.33%. - Facilitated product roll-out by analysing the performance of multiple products. Problems with products and reasons for poor customer engagement were discovered so that product managers were able to move forward knowing how to improve their products. - Created a geospatial software application in Python.

Statistical reports, process automation with VBA macros, account openings.

Thesis: Machine Learning Analysis of Maternal Stress Stimuli in Low-Income US Households

Thesis: Applied Machine Learning for Gastrointestinal Disease Diagnostics