Applied Scientist &
Computational Researcher

Bridging the gap between theoretical modeling and operational efficiency. With 8+ years of programming experience, I apply scientific rigor and machine learning to solve complex optimization problems in large-scale networks.

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The Scientific Approach to Operations

I am a Researcher and Applied Scientist with a foundation in Computational Chemistry and Physics. For over eight years, I have utilized programming languages like Python, C++, and Rust to model complex physical systems. I have since pivoted to applying these same methodologies—simulation, statistical modeling, and algorithmic optimization—to industrial logistics and supply chain networks.

My professional goal is to eliminate the barriers between raw data and actionable strategy. Whether optimizing molecular simulations or reducing transportation costs by millions, I thrive on building automated pipelines and predictive models that drive measurable efficiency.

8+ Years Coding (Python/C++)
$6.5M+ Total Cost Savings Delivered
PhD Level Research Background

Technical Competencies

Scientific Computing & Core

  • Primary: Python (8+ years), C++, C
  • Modern Systems: Rust (Memory safety & performance)
  • Libraries: Pandas, NumPy, SciPy, Scikit-learn
  • Math: Linear Algebra, Calculus, Statistics, Monte Carlo Methods

Data Engineering & Cloud

  • AWS Certified: Cloud Practitioner
  • AWS Stack: S3, Glue, Lambda, Redshift, Athena
  • Pipelines: ETL/ELT Architecture, PySpark
  • SQL: Advanced Querying, CTEs, Window Functions

Optimization & BI

  • Analysis: Root Cause Analysis, Process Simulation
  • Tools: Amazon QuickSight, Tableau
  • Outcome: OpEx Reduction, Workflow Automation

Professional Experience

Business Analyst

Amazon TOMY (Transportation Operations Management)

Oct 2025 - Present

Note: Operating in an Applied Science capacity focusing on network optimization and tool development.

  • Overhauled a legacy 3,500-line R codebase to improve maintainability and performance; implemented unit testing and optimized logic to reduce RAM usage from 512GB to less than 10GB while accelerating runtime from 2 hours to under 60 seconds.

Data Analyst

Amazon TOMY

Apr 2024 - Oct 2025
  • Algorithmic Cost Reduction: Engineered a higher-order predictive model enabling strategic network planning, resulting in $3.77MM in savings.
  • Asset Optimization: Conducted multi-stream data analysis to identify resource gaps, executing a strategy that recovered $2.8MM in underutilized assets.
  • Cloud Architecture Optimization: Refactored AWS usage patterns, reducing cloud costs by ~60% and saving upwards of $60K in annualized OpEx.
  • Automation: Wrote Python scripts to redesign the metric computation SOP, reducing computational workflow time from 5 days to 5 hours.
  • Designed automated workflows to streamline team communication and task management.
  • Authored and refined Standard Operating Procedures (SOPs) for data ingestion.
  • Built interactive dashboards in Amazon QuickSight to visualize real-time yard health and carrier performance.

Graduate Research Assistant (Computational Chemistry)

Middle Tennessee State University

2017 - 2023

Conducted extensive doctoral research modeling electron dynamics in molecular systems.

  • Utilized C++ and Python to develop algorithms for Monte Carlo simulations.
  • Applied statistical modeling techniques to theoretical chemistry problems, resulting in peer-reviewed publication.

Education & Certifications

Certifications

Amazon Web Services (AWS)

Certified Cloud Practitioner

Coursera / DeepLearning.AI

Machine Learning Specialization (In Progress)

Focusing on Supervised Learning, Advanced Learning Algorithms, and Reinforcement Learning.

Doctoral Studies in Computational Chemistry

Middle Tennessee State University (2020 - 2023)

Focus: Numerical methods, statistical mechanics, and algorithm development for molecular simulation.

Master of Science, Chemistry

Middle Tennessee State University (2017 - 2019)

Focus: Numerical methods, statistical mechanics, and algorithm development for molecular simulation.

Scientific & Engineering Projects

Quantum Simulation

Variational Monte Carlo Optimization

Engineered a variational simulation to approximate the Hydrogen ground state. Implemented Gaussian Basis Set expansions (STO-nG) and solved generalized eigenvalue problems to minimize energy error to within 0.1% of exact analytical solutions.

PythonLinear AlgebraBasis Sets

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Equations of State

Thermodynamic Non-Linear Optimization

Developed a robust regression engine to fit complex Equations of State (Van der Waals, Redlich-Kwong) to experimental P-V-T data. Utilized the Levenberg-Marquardt algorithm to optimize non-linear parameters and minimize Chi-squared error.

PythonLevenberg-MarquardtData Modeling

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