Building forecasting models that reshape how physical goods are made, moved, and sold—eliminating billions in waste across global supply chains.
Applied AI Scientist
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This is a customer-facing scientist role, sitting at the intersection of deep modeling work and high-stakes commercial conversations. You’ll design, train, and rigorously evaluate probabilistic forecasting systems in Python, then partner directly with operations and logistics leaders at major enterprises to prove those systems deliver value their existing tools cannot. The problems are messy, the data is real-world and imperfect, and the impact is measured in reduced overproduction, fewer stockouts, and dramatically leaner distribution networks. You’ll shape both the technical roadmap and the narrative—translating model behavior into business confidence without ever overselling what the math actually says.
What they're looking for
- 3–8 years building and deploying machine learning models in production, with fluency across the full Python data stack
- Comfortable leading technical discovery sessions with enterprise customers—listening for unspoken constraints, then mapping them to model design choices
- Deep intuition for time-series forecasting, probabilistic inference, or Bayesian methods applied to noisy, intermittent demand signals
- Clear, precise communication: you can write a rigorous model evaluation doc and deliver the same findings conversationally to a non-technical VP
- Bias toward ownership in a small, senior team—you’re equally willing to debug a data pipeline and to run a make-or-break customer workshop