Teaching assistant for 67-364 Practical Data Science

• Used PySpark & SQL to pull data sets of >10million rows from Amazon Redshift relational database

• Performed data analysis using pandas, python & SQL to extract business insights related to email activity

• Worked with Marketing & Consumer Analytics teams to explore how email cadence, email content, & consumer demographics influence email activity, visualized findings using matplotlib & seaborn

• Built a Logistic Regression Model & tuned features to predict consumer email activity with >88% accuracy

• Used model findings to propose new email campaign strategy that is catered to individual consumer needs & increases overall email open rate from 4.3% to 5.4%

• Extracted member and subscriber data from MailChimp to clean records and develop new database system

• Researched best practices to create several templates and strategies for acquiring sponsorships

• Tested and proposed social media management tools and task management software that best fit the company’s needs and budget