Lead a team of three data scientists focused on developing and implementing advanced analytics solutionsPrototype and guide the development of a Next Best Action platform to enhance advisor-client interactions and drive business growthOversee the creation and deployment of machine learning models, including: End Investor Churn prediction models to improve client retention Net New Asset Forecasting models for strategic planning and resource allocationPioneered the implementation of a Generative AI module that automates drafts of client communications for financial advisors, improving efficiency and consistencyConducted topic modeling initiatives to extract valuable insights for various business units, supporting data-driven decision-making across the organization

Deploying custom data science solutions utilizing techniques such as NLP, forecasting, attribution, and delivering via Shiny apps, APIs, and custom reports.

Maximized quote button clicks on insurance client websites by developing an image recognition solution to optimize layout template and creative content.Develop Marketing Mix and Multi-Touch Attribution models for variety of clients.Evaluate experiments testing display ad messaging, size, and timing using regression models and markov chain attribution.

Predict outcomes such as customer lifetime value and churn probability, direct marketing effectiveness (response and incremental margin).Develop, model, and report on metrics surrounding customer acquisition and retention. Specifically, the number of new customers acquired every month, and how many return after specific time periods. These findings are reported to c-suite executives on a monthly basis.Build interactive dashboards in Tableau to communicate advanced analytics findings as well as provide a self service analytics portal for business users.Manage products and projects from multiple analytics vendors, perform monthly ETL, help non-technical business users evaluate analytics vendors, Find and vet new vendors to quickly meet business needs.Tools: R, SQL, Hive, TableauTechniques: Linear & Logistic Regression, Random Forest, Gradient Boosting Machines, Deep Learning

Center for Human Resource ResearchCollaborate in developing an Early Warning System for Ohio public schools, an algorithm that uses real-time administrative data to assess risk of dropout and failed promotion.Curate Education Management Information System (EMIS) data, a database containing administrative data from all Ohio public schools from 2003 to present.Encourage use of data to outside researchers by assisting with IRB application and preparing data files.

Assessed equipment tracking practices by shadowing and interviewing relevant staff.Presented recommendations on how to improve equipment tracking process without increasing costs or headcount.

Soc 300: Methods of Research in Sociology. Conduct test review sessions. Assist students with homework assignments. Help students learn the statistical package SPSSSoc 307: Data Analysis & Presentation: Help students learn data management skills in Stata. Taught students basic Stata programming, and best practices of reporting data with Word and Excel

Interviewed local high school coaches to get data on their athletic apparel needs.