ML/AI - Supply Chain

Role overview

Build machine learning and statistical systems that improve planning and decision-making across a global hardware supply chain. This hands-on role spans problem definition, modeling, production deployment, and monitoring, with close partnership across planning, procurement, fulfillment, finance, sales, and hardware engineering. You will also help shape the team’s technical roadmap and modeling practices.

Responsibilities - Develop and deploy models for demand, inventory, lead times, supplier risk, costs, and cash-flow planning. - Combine ERP records, connected-device telemetry, market information, and operational data to create useful features and forecasts. - Build data pipelines and production services that support batch and real-time inference, then monitor performance and business impact. - Work with business and engineering partners to define data needs, integrate model outputs into workflows, and support adoption. - Improve the data and analytics foundations used for model training, monitoring, and inference. - Contribute to scientific direction and experimentation standards; review code and support junior scientists.

Requirements - Strong experience developing and deploying machine learning or statistical models in production. - Ability to work across the modeling lifecycle, including data preparation, validation, deployment, and monitoring. - Experience with large, varied operational datasets and building reliable pipelines for model use. - Sound judgment in ambiguous, cross-functional settings, with an ability to connect technical work to measurable business outcomes. - Clear communication and collaboration skills for working with technical and nontechnical partners.

Benefits and work setup

The role is remote for candidates based in the United States or Canada. The work involves a complex hardware supply network, variable demand, long supplier lead times, and operational signals that can inform planning and risk decisions.