About

Starting Semester: Fall 2025
Assigned: No
Location: New York City

GLOBAL OPS

Client Profile

GLOBAL OPS is a premier security services agency based in New York City, with a robust global presence. Specializing in personal protection, security consulting, risk analysis, and advanced safety measures, we utilize cutting-edge technology and industry-leading training to ensure unparalleled protection for our clients.



As part of our ongoing commitment to innovation, we have developed GOPS ONE, our latest technological advancement—a comprehensive enterprise resource planning (ERP) platform. GOPS ONE is designed to elevate the client experience, streamline operations, and enhance agent accountability and communication. This platform reflects our mission to transform the security industry through technology, ensuring that every client receives the highest standard of service.



With a growing demand for security solutions both nationally and internationally, and a continued focus on technological investments, GLOBAL OPS has experienced rapid expansion in recent years. We are poised to redefine the future of security, combining the best of human expertise with cutting-edge technology to deliver world-class protection and peace of mind.

Project Description

Problem:

Our monitoring team manually tracks and verifies coverage for nearly 1,000 shifts per week, leading to high labor costs, inefficiencies, and scheduling risks. The absence of automated tracking, standardized operating procedures (SOPs), and real-time communication between office and field teams creates operational bottlenecks. Additionally, callout management—including handling last-minute absences and replacements—lacks a structured, data-driven approach, increasing the risk of unfilled shifts, service disruptions, and security vulnerabilities.



Opportunities:

There is a significant opportunity to develop and implement a real-time monitoring and verification dashboard, empowering control room operators with data-driven insights for improved decision-making. By integrating anomaly prediction using machine learning models based on data from GOPS ONE, we can proactively identify potential issues, such as callouts or unfilled shifts, before they occur. Establishing a clear standard operating procedure (SOP) and providing operators with advanced tools will streamline manual processes, reduce time and costs, and improve resource allocation. This predictive approach will not only enhance operational efficiency but also mitigate security risks and ensure better client service.

Skills

The ideal team would need expertise in data analysis, data cleaning, and preprocessing using Python and SQL. Strong skills in machine learning for anomaly prediction (e.g., random forests, isolation forests, autoencoders) are helpful, along with experience in standard operating procedures (SOPs) design.