AI & Data Analytics Intern - Supply Resilience

Motorola SolutionsElgin, IL
Onsite

About The Position

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. 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.

Requirements

  • Currently pursuing a Bachelor’s degree in Supply Chain Management, Logistics, Business Analytics, Operations, or a related field
  • Graduation date not later then Dec.2029

Nice To Haves

  • Power BI, or Tableau for user interface creation.
  • Python or R, Scikit-learn, TensorFlow or PyTorch, Pandas, NumPy
  • Analytical problem-solving, independent initiative, ability to translate complex data into business insights
  • Understanding of logistics, lead times, procurement dynamics, and risk factors

Responsibilities

  • 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 Development: Design, train, and validate predictive algorithms (e.g., risk scoring, time-series forecasting, anomaly detection) to flag potential supply bottlenecks and vendor risks.
  • Tool & Interface Creation: Build a functional prototype/dashboard or integrate model outputs into an actionable tool for supply chain planners.
  • Market Research & Feature Engineering: Analyze macro-market trends to engineer features that improve model accuracy and early-warning alerts.
  • Documentation & Communication: Present model logic, accuracy benchmarks, and actionable insights to key stakeholders and operations teams.
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