Develop client-specific data solutions enhancing data-driven decision making and project efficiencyAutomate data analysis through custom Excel function creation and Shiny app deployment to ensure efficient time management and customer satisfactionTransform data to perform actionable insights across a diverse range of industry perspectivesCommunicate findings clearly and concisely regardless of model complexityProduce comprehensive reports, enabling clients to deliver data-driven recommendations within a given timeline

Facilitated grant proposal awarding by editing and critiquing a  proposal to construct several EV fast charging stations east of the Cascade Mountains in Washington State.  This project will greatly assist in achieving the state mandate that all vehicles purchased  or registered in the state must be EV by 2030.   Forecasted project cost of the EV charging station grant in an easily communicable and understandable  way regardless of the professional focus of the audience through visualizations using the matplotlib and seaborn Python modules. Investigated Funding Opportunity Announcements (FOAs) through federal (Office of Energy Efficiency and Renewable Energy) and state (Washington State Department of Commerce) entities and summarized technical aspects of the grants in easily communicable terms .

Successfully designed and executed a project that utilized unsupervised machine learning to identify the Raman spectra of certain phenolic compounds using Surface Enhanced Raman Spectroscopy (SERS) and supervised machine learning to quantify those compounds once identified. This pioneering work clearly demonstrates that SERS combined with machine learning can be used to model small organic molecules such as phenolics. Successfully designed and executed a project for tracking alcoholic fermentation of red and white wines using spontaneous Raman spectroscopy (SRS) and quantitative machine learning algorithms. This methodology greatly simplifies and expedites accurate tracking of fermenting wines by exposing said wines to a non-destructive laser beam and recording their subsequent Raman spectra. Successfully designed and executed a project focussed on quickly and accurately predicting the phenolic content of red wine by recording their UV-visible spectra. This work led to the development and deployment of Shiny apps using the Posit’s Shiny framework in commercial wineries in Washington State and California. With the apps, anyone can accurately predict the concentration phenolic compounds in their wine regardless of their level of expertise.Routinely mentored graduate students, edited graduate theses and manuscripts.Handled purchasing and reconciliation.Routinely analyzed and visualized data using the R and Python languages.Created posters and other media to gain interest in awarded grants and attract certificate students and participate in workshops.Wrote and edited manuscripts and submitted them for publication for my own work as well as other projects I was directly involved with.

Successfully designed and executed a project for predicting mean tannin polymer size in red wines by constructing a model using tannins that were first isolated from raw cacao beans using flash chromatography and preparative High Performance Liquid Chromatography (HPLC). By isolating polymers of specific sizes, this work allows for more accurate prediction of tannin polymer size in red wines which is directly correlated with wine longevity and hence cost. Routinely analyzed and visualized data using the R language to fulfill various project requirements.Wrote and published manuscripts of my work in accordance with journal format requirements.