Joshua Nicholson

Williamston,MI

Summary

Data scientist with a strong foundation in machine learning, statistical modeling, and quantitative analysis, built on a rigorous academic background in physics. One year of professional experience applying XGBoost and regression techniques to large-scale, sparse, and noisy detector data in a research setting. Proficient in Python, exploratory data analysis, and model evaluation. Known for translating complex mathematical insights into clear, actionable outcomes and for bridging technical depth with strong cross-functional communication.

Overview

year of professional experience

Work History

Machine Learning Research Scientist

STAR at Brookhaven National Lab / Abilene Christian University

06.2022 - 06.2023

Built predictive models using XGBoost to reconstruct true distributions from large scale noisy detector simulations and data
Enhanced sparse training data by fitting smooth functions to undersampled regions, enabling better generalization in high-noise, low-data conditions
Collaborated in a team with doctoral researchers using neural networks, benchmarking performance across our modeling approaches
Communicated results with clear visualizations and summaries to guide team research and validate model behavior
Wrote reproducible, modular Python code with structured documentation for team reuse and iterative development
Applied mathematical modeling and creative problem-solving daily

Education

M.S. - Computational Physics

Michigan State University

12.2025

Skills

Proficient in Python
Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, XGBoost, SQL
Advanced Mathematical Rigor
Machine Learning & Data Analysis
Supervised learning techniques

Advanced computational modeling
Data Visualization
Critical Thinking and Problem Solving
Proficient in scientific documentation
Microsoft Excel, Power Point

Accomplishments

University Scholar - Graduated top of undergraduate class