Curriculum Vitae

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Professional Summary

Data Scientist with 14+ years specializing in machine learning, predictive modeling, and enterprise data visualization across higher education, national security, and biometric research. Created visualization standards adopted by 28% of 14K workforce and led development of enrollment prediction systems serving 15-20 clients. Expert at translating complex analytics into compelling visual narratives for C-level executives and technical teams. Recent focus on cutting-edge AI applications, successfully delivering LLM-powered products and data-driven enrollment strategies.

Technical skills

Programming: R, Python, SQL, MATLAB, SAS
Machine Learning: Scikit-Learn, LLM Fine-tuning, Prompt Engineering, Statistical Modeling
Visualization: R Shiny, ggplot2, Matplotlib, Tableau
Infrastructure: Docker, Git, AWS, CI/CD Pipelines
Research: Experimental Design, Hypothesis Testing, Applied Statistics

Professional Experience

Data Scientist, Capture Higher Ed, Louisville, KY, 08/2022–Present
As a remote member of Capture Higher Ed, a data-driven enrollment management and marketing firm that helps colleges and universities identify and enroll prospective students; I designed, deployed, and maintained internal- and partner-facing machine learning based applications in R and Python on AWS. Advised nine higher education clients on utilizing our tailored models to achieve their annual enrollment goals and determine financial aid awards. Supported the internal R packages and Docker-based development environments through maintenance and feature enhancements.

  • Awarded company-wide Rookie of the Year (2023) by peer vote, selected from 21 candidates across two hiring years for consistent high performance and impact across multiple project areas.
  • Rapidly prototyped 2 LLM-powered products (chatbots and content generation) within 6-month deadline using OpenAI models, prompt engineering, fine-tuning, and RAG, maintaining market competitiveness with products entering general availability in 2025.
  • Developed R Shiny application that automated model performance analysis for 15-20 higher education clients, expanding evaluation from 3 manual time points to comprehensive analysis of 15 prediction points across 5 individual models, enabling identification of seasonal patterns and data-driven prediction improvements.
  • Modernized historical apply report system by consolidating scattered R scripts into main package infrastructure, eliminating single-person bottleneck to enable all 4 team members to generate client on-boarding reports and reducing generation time from 2 days to 4 hours.
  • Assumed ownership of containerized development environment supporting 4-person data science team’s production model deployment, implementing ARM architecture support to enable migration to faster machines and maintaining annual security updates for R/Python libraries and federal data sources.

Data Scientist, Sandia National Laboratories, Albuquerque, NM, 07/2018–08/2022
At Sandia National Laboratories, a federally funded research and development center advancing national security and technology innovation, I developed and productionized two machine learning models and four interactive data visualizations to improve safety, security, and employee retention. I provided ad-hoc data analysis to leadership on the performance of the $177.7 million Laboratory Directed Research & Development (LDRD) program. Collaborating with data engineers and software developers, I enhanced access and maintenance for 83 enterprise data sets, improving data usability across teams. Additionally, I established best practices in research software development by creating internal software packages and documenting CI/CD pipelines with Docker and GitLab.

  • Won competitive CIO funding (1 of 3 from ~20 proposals) to co-develop data visualization style guide, achieving 28% adoption across 14,000-person workforce as measured by website analytics.
  • Co-leader of the five-person data visualization sub-team and technical leader of the four-person UI/UX data visualization style guide team.
  • Developed R Shiny web interfaces for machine learning predictive models to reduce the number of safety and security incidents.
  • Developed three internally used R packages to simplify and standardize database connections, establish visualization standards, and package management.

Data Analyst, HID Global, Albuquerque, NM, 01/2012–06/2018
As a member of HID Global, a leading provider of secure identity solutions specializing in products and services that enable trusted access to physical and digital spaces. I managed the human testing lab; coordinated 53 research studies, in various sites in the US to evaluate biometric sensors. Analyzed resulting data to measure biometric sensor performance and prepare it for machine learning algorithms. Oversaw 12.89 terabytes of collected data. Presented research findings using various media (print, video, and webinars) to both technical and non-technical staff, and clients. Researched and identified new methods for spoofing biometric devices.

  • Collaborated with hardware engineers and computer scientists, on the research and development of four biometric sensors; including one for guests accessing the most visited vacation resort in the world.
  • Identified three unknown security threats to HID Global biometric sensors that led to improvements in our fake finger detection algorithm.
  • Cut research study cost and product development time by improving subject recruitment; identified new recruitment methods, and improved communication that led to 48% subject retention rate.
  • Engineered a data pipeline to process 3,000 videos from wireless security cameras collected daily throughout research sites in the United States for an object detection algorithm.
  • Decreased algorithm development time and improved data quality by implementing new filename conventions, and creating a MATLAB accessible MySQL database to index collected data.

Data Analyst, NMSU Department of Public Health Sciences, Las Cruces, NM, 08/2010–05/2011
While attending classes full-time, I actively assisted with a variety of health based research projects within the Department of Public Health Science. Provided statistical consulting to 8 graduate students and 11 faculty members at all stages of the research life cycle: grant writing, hypothesis development, survey and experiment design, preparing data, using statistical software to analyze data, and interpreting results for presentation or publication.

  • This position was created specifically for me, after the department head was impressed by my knowledge of applied statistics, programming ability (in SAS and R), and communication skills.
  • Authored a presentation with the university’s associate dean of research on using a new metric to measure sexual confront in college students.

Student Statistical Consultant, NMSU Department of Economics, Applied Statistics, and International Business, Las Cruces, NM , 08/2010–05/2011
Provided statistical support to graduate students and university staff by advising them on statistical analysis methods and software. Analyzed data with SAS and R. Worked on projects regarding: MBA admissions criteria, crime rates in motels, and dating websites.

Intern Statistician, USDA Food Safety Inspection Service, Washington, DC, 04/2010–08/2010
Ensured quality of raw data collected by the Food Safety and Inspection Service in meat, poultry, and processed egg product facilities throughout the United States. Cleaned raw data and updated data warehouse. Used statistical methodologies to estimate the prevalence of foodborne pathogens and prepared relevant figures for professional presentations.

  • Authored a report for the public on the prevalence of pathogens in raw poultry products.
  • Consulted with other interns from non-technical backgrounds on how to properly use statistical techniques on their work-related projects.

Education

MS in Applied Statistics, New Mexico State University, Las Cruces, NM, 2011

BS in Biology (Minor: Applied Math), New Mexico State University, Las Cruces, NM, 2009

Certification

Fundamentals of Computing by Rice University on Coursera. Certificate earned at July 7, 2018

Applied Data Science with Python by University of Michigan on Coursera. Certificate earned at September 28, 2017

Professional Training

Cloudera Data Scientist Training by Cloudera. 02/14/2022–02/17/2022

SAFe® Product Owner/Product Manager by Scaled Agile. 01/11/2022–01/13/2022

Storytelling with Data by Cole Nussbaumer Knaflic. 10/15/2020

JavaScript for Shiny Users at RStudio Conference 2020. 01/27/2020–01/28/2020

Apache Spark™ Programming (DB 105) by DataBricks. 09/24/2019

Presentations

Viz Wars: Tableau vs. Shiny, National Laboratories Information Technology Summit. Boise, Idaho. 04/29/2019

Assure for Safety, National Laboratories Information Technology Summit. Boise, Idaho. 04/28/2019

Conferences

Posit Conference 2024 by Posit, Virtual. 08/12/2024.

Posit Conference 2023 by Posit, Virtual. 09/19/2023.

RStudio Global Conference 2022 by RStudio, Virtual. 07/25/2022.

RStudio Global Conference 2021 by RStudio, Virtual. 01/21/2021.

DOE Virtual Data Days 2020 by The Department of Energy, Virtual. 10/05/2020–10/07/2020.

Spark + AI 2020 Summit by DataBricks, Virtual. 6/22/2020

Conference on Data Analysis by Los Alamos National Laboratory, Santa Fe, NM. 02/25/2020–02/27/2020.

RStudio Conference 2020 by RStudio, San Francisco, CA. 01/29/2020–01/30/2020

National Laboratories Information Technology Summit by Federal Business Council, Inc. Boise, Idaho. 04/28/2019–04/31/2019