Staff / Senior Applied Scientist — Operations Research

We are hiring a Staff or Senior Applied Scientist to build and deploy advanced AI/GenAI and ML systems grounded in applied mathematics and Operations Research (OR). This role is ideal for someone who can take ambiguous, high-impact operational problems, turn them into clear mathematical/algorithmic formulations, and lead implementation end-to-end—including writing production-quality code.

You’ll work at the intersection of optimization, inference science, NLP, and reinforcement learning, with a strong emphasis on practical delivery and measurable business outcomes. Supply chain optimization experience is a plus.

• Problem formulation: Translate business challenges into mathematical and computational models.
• Applied research: Evaluate and adapt existing approaches (OR models, AI/ML, GenAI/LLM methods).
• Algorithm development: Design and implement new algorithms when existing solutions fall short.
• Hands-on execution: Build prototypes, run experiments, and lead production implementation (you code).
• Technical leadership: Drive solution architecture, performance evaluation, and operational readiness.
• Collaboration: Partner with product, engineering, and domain teams to deliver scalable solutions.

• Strong applied mathematics background with depth in Operational Research, including one or more of:
• Linear / Mixed-Integer Optimization (LP/MIP)
• Constraint Programming
• Network / Graph Optimization
• Stochastic / Robust Optimization
• Simulation, heuristics, or metaheuristics
• Research or applied experience in at least one of:
• Inference science (e.g., causal inference, Bayesian methods, uncertainty modeling)
• NLP (e.g., retrieval, embeddings, information extraction, LLM systems)
• Reinforcement Learning (e.g., bandits, sequential decision-making, offline RL)
• Proven ability to implement algorithms and deliver real systems end-to-end.
• Strong coding skills in Python and familiarity with relevant scientific/ML tooling.

• Experience in supply chain optimization (planning, inventory, logistics, fulfillment, scheduling).
• Experience building GenAI/LLM-enabled decision systems (e.g., RAG, tool-augmented agents, evaluation frameworks).
• Background shipping applied science solutions into production environments.

• PhD (preferred) or MS in Applied Math, Operations Research, Industrial Engineering, Computer Science, Statistics, or equivalent industry experience.
• Demonstrated ability to combine scientific rigor with practical execution and delivery.

• High-impact, real-world optimization and decision problems.
• Freedom to apply the best approach: OR, ML, RL, GenAI, or hybrid methods.
• Senior technical ownership with a strong hands-on build-and-ship expectation.