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Department of Applied Economics, University of Minnesota.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Minnesota
Data Scientist
June 29, 2026 – Present
GEP-WaterUse-Agriculture
June 19, 2026 – Present
Code for GEP computation for Water Provisioning Services for Agriculture
View ProjectSample-Reproducible-Project-stata
April 2, 2026 – Present
Sample reproducible project (with stata) created for workshot at the APEC Student Seminar for demonstration purposes.
View ProjectReproducibility-Workshop
March 26, 2026 – Present
Reproducibility-Workshop — GitHub repository
View ProjectGEP_GWR
July 14, 2025 – October 31, 2025
Code for GEP computation for Groundwater Recharge
View ProjectStaggeredDID-lab
June 4, 2025 – July 16, 2025
Materials and code for understanding staggered DID estimators
View Projectdynamic_optimization
September 17, 2024 – September 17, 2024
Code practicing dynamic optimization
View ProjectShunkei3.r-universe.dev
September 6, 2024 – September 6, 2024
Shunkei3.r-universe.dev — GitHub repository
View ProjectR-2024-Summer
July 23, 2024 – August 23, 2024
This is the repository for the Introduction to R Statistical Analysis Software (Summer, 2024) taught in the Applied Economics department at the University of Minnesota. You can find all the slides and the documents for the course here.
View ProjectVRA_with_CF
February 20, 2022 – July 7, 2022
This repository has the R and Python codes and data related to manuscript "Causal Forest Approach for Site-specific Input Management via On-farm Precision Experimentation." by Kakimoto, S., Mieno, T., Tanaka, T.S.T., & Bullock, D.S.
View ProjectCultural Fit Analysis
The candidate's projects are primarily academic and research-oriented, focusing on reproducibility and statistical analysis. While these skills are relevant to a Data Scientist role, the breadth of application outside of academic research is not clearly demonstrated. The current role as 'Data Scientist' at the University of Minnesota aligns with the target role, but the lack of diverse industry projects or team-based initiatives makes it difficult to fully assess cultural fit for a broader organizational context. The technologies used are heavily skewed towards statistical programming (R, Python, Stata, Matlab) and document preparation (TeX), with less emphasis on typical software engineering practices often found in industry data science roles.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. The candidate's project descriptions are concise, but there's no direct evidence of communication style, teamwork, or problem-solving approaches in a professional setting.