Nathan Bergman

nathancbergman03@gmail.com | (570) 460-3915
github.com/nathanbergman | linkedin.com/in/nathanbergman123

EDUCATION

Penn State University, The Behrend College

Computer Science, Bachelor of Science | Graduated: May 2025

GPA: 3.0

TECHNICAL SKILLS

WORK EXPERIENCE

Java Intern, Back-end Developer - Revature

Pittsburgh, PA | May 2025 - August 2025

  • Completed intensive backend development training focused on Java, RESTful APIs, and enterprise frameworks.
  • Built and tested RESTful backend services using Java, Spring Boot, Javalin, JDBC, and SQL.
  • Wrote unit and integration tests using JUnit and Mockito; practiced version control with Git and Maven-based builds.

Service Leader - Chipotle

Canonsburg, PA | September 2025 - Present

  • Led and supervised front- and back-of-house operations in a high-volume restaurant, ensuring food safety compliance, team coordination, and consistent customer service.
  • Trained and supported crew members during peak hours to maintain efficiency and accuracy.

ACADEMIC/PERSONAL PROJECTS

Raspberry Pi Systems Project

Winter 2026 | Linux, GPIO hardware, Network Services

  • Built and configured a Raspberry Pi system running Linux to support custom software, GPIO hardware integration, and basic network services (SSH, system configuration, scripting).

Goblin Fall - Android Mobile Game

Winter 2025 | Kotlin, Jetpack Compose, Firebase AdMob

  • Designed and developed a 2D Android arcade-style game using Kotlin and Jetpack Compose.
  • Implemented real-time falling mechanics, collision detection, score tracking, and Firebase AdMob monetization.

Scavenger Hunt App

Fall 2024 - Spring 2025 | React Native, MSSQL, C#, Javascript

  • Developed a mobile/web scavenger hunt application to help first-year students explore campus.
  • Implemented location-based challenges and database-driven progress tracking using React Native, MSSQL, and C#.

User Behavior Modeling for Personalized Ads

Spring 2025 | Python

  • Predicted ad click-through conversions using the IBM Synthetic Advertising dataset.
  • Built and compared machine learning models, including LightGBM, XGBoost, Balanced Random Forest, MLP, and stacking ensembles.