A researcher/developer specializing in large language models
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Entrepreneurship can happen inside an organization. If I see how things can be done better, I go for it, and I encourage that attitude among my teammates and direct reports. My motto: if you can automate yourself out of a job, do it. I guarantee if you're looking for one you'll find another place where you can contribute your newfound free time.
North Carolina State University
Doctor of Computer Science, Computer Science
January 1, 2011 – January 1, 2016
Earlham College
Bachelor of Arts, Computer Science
January 1, 2006 – January 1, 2010
SAS
Senior Research Statistician Developer
October 1, 2021 – Present
SAS
Research Statistician Developer, Artificial Intelligence R&D
July 1, 2015 – October 1, 2023
North Carolina State University
Natural Language Processing Researcher and Pedagogical Software Engineer
August 1, 2011 – May 1, 2016
Raleigh-Durham, North Carolina Area
Shodor Education Foundation, Inc.
Software and Curriculum Developer
June 1, 2010 – June 1, 2011
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research and AI/NLP development within a large enterprise (SAS) and university settings. While this demonstrates deep technical expertise, the projects listed do not explicitly align with typical 'Backend Engineer' roles that often involve distributed systems, microservices, API design, and database optimization outside of an AI/ML context. The focus on NLP models and data science frameworks suggests a strong fit for an AI/ML Backend Engineer role, but less so for a general backend role without further evidence of broader backend development skills. The lack of diverse project types outside of AI/education might indicate a narrower cultural fit for a general backend team.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a strong ability to work in R&D environments, contribute creatively, and manage complex projects. Their work at North Carolina State University involved both research and software development, indicating a blend of analytical and practical skills. The description of building a 'world-class data science framework' implies strong problem-solving and architectural thinking. However, without specific project details or team collaboration examples, it's difficult to fully assess operational fit beyond a research-oriented role.