Professional Experience

Machine Learning Engineer & Data Scientist Co-Op

RXA: AI & BI Consulting —–

• Website
• January 2021 – Present
  • Developed Gradient Boosted ML Model on custom dataflow in DOMO [SQL, Python] structuring 60,000+ sales records optimizing client automotive acquisition for maximizing expected profit margin with 96% accuracy
  • Created API [Python, Google Cloud API] to aggregate and structure ~4GB historic client sales CSV data from drive repository for 100+ files with full client encryption and recurring database updates saving ~30+ hrs. data entry
  • Implemented ML hierarchical clustering model [Python, R, AWS Sagemaker] on client’s insurance case data with AUROC of 0.88 thus identifying 7 key consumer risk factors potentially increasing policy profit margins 30%

Machine Learning Dev. Student Research

Computational Ecology, Evolution and Biology Lab - University of Michigan —–

• Lab Website
• January 2020 – Present
  • Published open-source R package titled weightedClustSuite [R, C++, Python] for Machine Learning Density Clustering & Validation, used on weighted species abundance data enabling identification of species distribution clusters Link
  • Applied weighted Species Trait Data to 2014 ML Research in Density Peak Clustering demonstrating contrary coexistence of similar niched species despite competitive exclusion with promising clustering validation (DBI: 0.56)
  • Operate as an interdisciplinary reference within lab to explain and analyze machine learning and data analysis research. *My Code

Product Director & Founder

Michigan Eco Data —–

• Org Website
• September 2019 – Present
  • Product Manager Execution of consumer heatmap awarded 2000$ sponsorship by Michigan Institute of Data Science (MIDAS)
  • ● Built dynamic web dashboard software of interactive Michigan Heatmaps [HTML, JS, Python] (My Code) from 400,000+ Unstructured EPA data points for Michigan Residents to identify contaminants at high levels in their zip codes
  • Founded org as a venue for interdisciplinary exploration in data science, software engineering, and environmental conservation
  • Other Projects Include: Ecological Footprint App Tracking and Wildlife Drone Pattern Analysis
  • Notable Technologies Used: Python [Pandas, Date-Time Forecasting, PyPlot, Matplotlib], R, HTML, CSS, Jinja, GitHub Pages, Swift, MAVSDK

Data Science Intern

GHD: Engineering, Architectural, and Environmental Consulting —–

• Company Website
• June 2019 – August 2019
  • Implemented Automation pipeline [Python, FME] from custom field forms into my designed database [SQL] auto filling Client Audits resulting in an 80% reduction in manual data entry for Client EPA Facility Audits
  • Deployed ML: Random Forest & Regression Models on structured ARCGIS data determining optimal PFAS site testing order for estimated 75% reduction in location sampling costs
  • Notable Technologies Used: ARCGIS, ARCGIS External API, SAP-HANA, Python, Survey123, FME, MS Excel

Machine Learning & Computer Vision Student Research

Michigan Medicine Computer Aided Diagnosis Laboratory - University of Michigan —–

• Lab Website
• January 2017 – May 2019
  • Findings resulted as Primary Author in two Publications, was selected for Poster, Oral Presentation in SPIE Conference 2019
  • Performed Feature Extraction and Selection on Bladder CTU Scan Segmentations to extract key features in Bladder Cancer Lesion Staging (staging determines the avenue of treatment)
  • Implemented and trained a series of Back Propagated Neural Networks, Linear Discriminant Analysis, Support Vector Machines, and Random Forest Algorithms – validated through ROC analysis - with 92% suite ROC Sens. Accuracy for accurate staging diagnosis of Bladder Cancer determining immediate treatment
  • Notable Technologies Used: C++, C, Python, Weka, MS Excel
  • My Work Samples