Data Scientist

The AAR Parts Supply Distribution Analytics Team are developing an AI-driven aviation market intelligence tool designed to transform how aviation parts distribution businesses understand market share, identify growth opportunities, and make strategic decisions. This platform combines advanced analytics, machine learning, agentic AI, and intuitive user experience to provide real-time insights into customers, parts, and markets. 

The Data Scientist will define, build, and continuously improve the analytical and machine learning models that power the platforms core decision-making capabilities. This role translates complex business problems into scalable, production-ready models that drive market sizing, forecasting, and opportunity identification. The Data Scientist partners closely with product, engineering, and data teams to ensure models are accurate, explainable, and embedded into real-world workflows. Success in this role requires strong technical depth, business intuition, and the ability to operate in ambiguous, data-rich environments. 

This position is based at our Corporate Headquarters in Wood Dale, IL, with a planned relocation to the Merchandise Mart (Chicago) in early 2027.

What you will be responsible for: 

• Design, develop, and own scalable analytical and machine learning models for market sizing, forecasting, opportunity identification, and optimization use cases. 
• Translate ambiguous business problems into structured modeling approaches, including feature engineering, model selection, and evaluation frameworks. 
• Design and analyze experiments, statistical tests, and validation methods to measure model quality and business impact. 
• Deploy and integrate models into production systems in collaboration with data engineering and backend teams, ensuring reliability, scalability, and performance. 
• Work with large, complex, and imperfect datasets; define data requirements and support robust preprocessing and feature pipelines. 
• Ensure model outputs are explainable, interpretable, and aligned with business logic to support user trust and adoption. 
• Monitor, validate, and improve model performance through testing, retraining, versioning, and feedback loops. 
• Partners with product teams to define analytical features, influence roadmap decisions, and embed model-driven insights into decision-making workflows.