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Lead data scientist for innovative proofs of concept and projects using a variety of statistical and machine learning techniques for prediction and clustering (Deep Learning, Neural Networks, XGBoost, Feature Selection/Engineering, linear modeling, forecasting, time series analysis, anomaly detection, mixture models, K-means).Led ideation/use case definition, data integration, data preparation, model development, and model deployment), ensuring satisfaction of business case goals and delivering understandable results that could easily be shared with leadership and non-technical individuals.Initiatives include: Manufacturing for the Future (downtime reduction), Customer Clustering and Anomaly Detection, Market Segment Prediction, and other ad hoc models/analysis

“Modeling errors in linked survey and administrative data”The differences in reported values between two sets of data were measured and modeled using clustering models (mixture models) and Hidden Markov models in R (GPU processing with CUDA) and Python . Models were used to predict how future measures may be distributed and when errors are likely to occur which was corroborated and stress tested using simulations.

Co-Lead the development of medical receivables investment which required researching (labor code- SB863, legal journals and criticism, case law, medical journals, etc.), organizing (finding and training support staff, scrubbing data, evaluating software to help facilitate best practices), initiating (opening a new office, creating work flows, learning and teaching a new proprietary software) and analyzing [cash flow analyses, statistical analyses of AR (using Excel and R), and quality assurance]. This position required me to engage in a range of different capacities, wherever and doing whatever was required on the day to day.

Analyzed investment contracts to parse and analyze specifics against known market internal rates of return and efficient model analysis based on VBT table, valuated in batches for increased investment information using proprietary software (Milliman) to normalize expected ROI over time, formulaic excel analyses to model shadow account performance, Milliman, some Visual Basic (using Microsoft Access databases), Data Gathering, HIPAA Compliance.

As a research assistant for a social psychology professor, my responsibilities lead me to be engaged in research, helped develop my data analytical abilities (primarily in SPSS), introduced me to physiological equipment and software, and enhanced my writing in APA style.

The students chosen for the Senior Leadership Council were selected from the top 10% of the senior class based on their exceptional service and leadership contributions to the university. As members of the council, we were tasked with planning and informing our senior peers about events, volunteer opportunities, and responsibilities as alumni.

Applied theories on racial and ethnic voting behaviors to the study of the 2012 presidential election, which required researchers to investigate voting behaviors for a specific racial, ethnic, or gender group, predict outcomes based on these patterns, work with lead researchers to create survey questions, collect exit polling data on election day, enter polling data using Qualtrics, analyze data using descriptive and light inferential statistics in SPSS, collate into a final report on methodologies, prediction outcomes, and possible explanations of voting behavior.

In my time at Scripps, I assisted Account Executives in developing a portfolio of potential advertisers for the HGTV and DIY networks. I devoted my time towards building a holistic advertising plan that included potential leads, demographics surrounding those leads, evidential market research (from MRI), and ideas for marketing campaigns. This research was then presented to the HGTV/DIY West Coast Ad Sales team and later used as a sales tool.