Running, Basketball, Soccer, Weightlifting, Cooking, Reading, Writing
Steven Coyan
Laguna Niguel,CA
Summary
Data Scientist with a background in mechanical engineering and experience in statistical modeling, behavioral health analytics, and machine learning. Skilled in Python, SQL, R, and cloud computing platforms. Proven success in collaborating with academic researchers and developing interpretable models for real-world applications.
Overview
5
5
years of professional experience
1
1
Certification
Work History
Data Scientist
UCI Department of Emergency Medicine and Informatics
09.2024 - Current
- - Conduct statistical analysis of longitudinal datasets using Python and associated libraries (e.g., pandas, seaborn, statsmodels) to evaluate the impact of behavioral health interventions over time
- - Identify demographic patterns to assess the effectiveness and weaknesses of targeted interventions
- - Manage and process datasets to ensure accurate modeling and effective visualizations
- - Present and collaborate with PhD candidates and postdoctoral researchers to interpret findings and co-author academic publications
Machine Learning Instructor
The Coding School
06.2023 - 08.2023
- - Led labs and coding workbooks; provided a positive student experience centered around asking questions and learning
- - Educated students on EDA, machine learning, and deep learning concepts and applications
- - Developed strong teaching and communication skills through structured lessons and class collaboration
Machine Learning Engineer
Mental Health Model Project
09.2022 - 12.2022
- - Built an artificial neural network that predicts life satisfaction; MAE of 1.15 on target between 0–10
- - Applied EDA, PCA, and data visualization to preprocess CDC survey data
- - Developed skills in Python, GitHub, data mining, and model interpretation
- - Presented findings through weekly reports, design reviews, and team meetings
Research Assistant
Vance Lab & CU SPUR
06.2021 - 05.2022
- - Studied accuracy of low-cost and research-grade sensors in detecting PM2.5 particles in indoor environments
- - Evaluated effectiveness of portable air cleaners in reducing air pollution exposure
- - Built linear regression models explaining up to 60% variance for low-cost sensors during cooking periods, 88% for non-cooking
- - Presented findings to CU SPUR faculty and program participants
Education
M.S. - Data Science
University of Colorado Boulder
Boulder, CO12-2023
B.S. - Mechanical Engineering, Engineering Management Minor
University of Colorado Boulder
Boulder, CO05-2022
Skills
- Technical Languages: Python, R, SQL, MATLAB, EES
- Data Tools: pandas, seaborn, statsmodels, scikit-learn, PySpark, Databricks
- Cloud/DevOps: AWS, Docker, Git/GitHub
- Core Competencies: Python Programming, SQL Databases, Machine Learning, Statistical Analysis
Certification
Apache Spark (TM) SQL for Data Analysts
AWS Cloud Technical Essentials
Interests
Timeline
Data Scientist
UCI Department of Emergency Medicine and Informatics
09.2024 - Current
Machine Learning Instructor
The Coding School
06.2023 - 08.2023
Machine Learning Engineer
Mental Health Model Project
09.2022 - 12.2022
Research Assistant
Vance Lab & CU SPUR
06.2021 - 05.2022
B.S. - Mechanical Engineering, Engineering Management Minor
University of Colorado Boulder
M.S. - Data Science
University of Colorado Boulder
PUBLICATIONS
- DOI: 10.1039/D2EA00025C — "Assessment of PM2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner"
- DOI: 10.1039/D2EA00099G — "Aerosol emissions and their volatility from heating different cooking oils at multiple temperatures"