AI & Data Analytics Intern - Supply Resilience

Company Overview At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future.

Department Overview Seeking an innovative, data-driven AI & Data Analytics Intern to lead the development of an intelligent, predictive supply chain risk assessment tool. In this role, you will leverage internal enterprise data, external market intelligence, and advanced machine learning models to build an AI solution capable of forecasting supply chain disruptions before they impact operations.

Job Description

Are you ready to build data-driven solutions that protect and strengthen global supply chains? As the AI & Data Analytics Intern - Supply Resilience, you will integrate internal metrics—such as lead times, supplier performance, and inventory levels—with external market data, including geopolitical risks, weather events, commodity prices, and trade tariffs. In this hands-on role, you will design and validate predictive machine learning models (such as risk scoring, time-series forecasting, and anomaly detection) to flag supply bottlenecks and vendor risks before they occur. Using tools like Python/R and Power BI or Tableau, you will transform model outputs into actionable dashboards for supply chain planners and communicate key insights to business stakeholders.

This internship includes

Data Integration & Pipeline Development: Identify, aggregate, and ingest internal supply chain metrics (lead times, supplier performance, inventory levels) alongside external market data (geopolitical risks, weather events, commodity price trends, trade tariffs). AI/ML Model De