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Nicholas Pinto

Nicholas Pinto

Gettysburg, PA · (203) 939-2033 · scout.pinto@gmail.com

GitHub · LinkedIn

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Education

Gettysburg College Gettysburg, PA

B.S. in Computer Science, Minor in Mathematics, Data Science — GPA: 3.75/4.0

Expected May 2027

Technical skills

Languages: Python, Swift, Java, PowerShell

Technologies: SwiftUI, Firebase, Cloud Firestore, REST APIs, Git, GitHub, Azure

Libraries: NetworkX, Pandas, NumPy, SciPy, Matplotlib, pytest

Professional experience

Sullivan & Cromwell LLP New York, New York

June 2026 – Present

Electronic Discovery & Litigation Support Intern

  • Developed PowerShell-based workflow automation solutions within AgilityBlue to streamline data disposition processes, reducing manual coordination across legal, technology, and information security teams.
  • Integrated AgilityBlue REST APIs to automate project lifecycle tasks and eliminate bottlenecks within electronic discovery workflows.
  • Researched and evaluated emerging technologies, including Azure services, AI-assisted review platforms, and modern communication tools, contributing findings to quarterly firm-wide technology briefings.
  • Evaluated and improved data management workflows across the electronic discovery lifecycle with litigation support professionals.

Projects

DOBLE – iOS Emergency Medical Services Application

Jun 2025 – September 2025

Lead Developer, Darien EMS – Post 53

  • Swift
  • SwiftUI
  • Firebase Authentication
  • Cloud Firestore
  • Led design, development, and deployment of a full-stack iOS platform used by 130+ EMS personnel, centralizing scheduling, attendance, training resources, communications, and organizational records.
  • Designed and implemented the application architecture using Swift, SwiftUI, Firebase Authentication, and Cloud Firestore, enabling secure access control and real-time data synchronization.
  • Deployed the application through Apple's private distribution program and achieved 92% organizational adoption, replacing multiple third-party services while significantly reducing software costs.

YouTube Recommendation Network Analysis

  • Python
  • NetworkX
  • Pandas
  • NumPy
  • SciPy
  • Matplotlib
  • pytest
  • Built an end-to-end Python analytics pipeline to analyze 7,079 YouTube channels and 401,384 recommendation relationships, constructing a weighted network for large-scale ideological drift analysis.
  • Developed reproducible Monte Carlo-style weighted random walk simulations to model recommendation behavior and measure changes in ideological direction, extremity, assortativity, and clustering.
  • Developed a test-driven Python codebase with 72 automated tests, reproducible simulation results, and clear documentation to support reliable experimentation and analysis.

Leadership & awards

Secretary, ACM, Gettysburg College

Jan 2025 – Jan 2026

Vice President, Darien EMS – Post 53 — Town of Darien Exemplary Leadership Award

Sophomore Inductee, Pi Mu Epsilon Mathematical Honor Society