US Cold owns and operates one of the most complex temperature-controlled logistics networks in North America. Every day, our systems coordinate the storage and movement of food at national scale across a network of state-of-the-art distribution centers, including multiple highly automated warehouse facilities. We continue to advance our core warehouse and logistics platforms. Our current focus is on modular, event-driven, API-first and cloud architectures. We continue to enhance reliability and accelerate engineering productivity by strengthening our SRE and AI practices. This is a large investment in innovation to continue to drive operational excellence at our facilities. If you want to build durable systems that operate in the physical world at scale, this is that opportunity. We are hiring a Data Scientist to grow the capabilities of the organization to drive innovation, support data-driven decision making, and transform both business and operational practices. You will solve problems using statistical methods, machine learning, and AI. You will help the organization frame and answer analytical questions grounded in data. You will participate in research and development projects. This role is ideal for a hands-on, applied data scientist who enjoys working on complex systems, translating ambiguous business problems into analytical models, and seeing their work put into production. You’ll build models that power live warehouses, moving freight, and real decisions—improving the system every single day. You will help modernize the analytical and decision‑making backbone of a company that moves food every day. You will shape durable, production‑grade data science systems that operate under real‑world constraints, not toy datasets or offline experiments. The Kind of Problems You’ll Solve · Translating complex logistics and operational workflows into models that drive real decisions · Designing forecasting, optimization, and anomaly‑detection solutions that remain reliable under operational load · Embedding models into production systems where latency, uptime, and data quality matter · Building observability into models so performance, drift, and impact are measurable from day one · Modernizing legacy analytics and heuristics without disrupting live operations This role is for someone who prefers substance over hype. You are comfortable operating where models must work every day—not just in notebooks—and where imperfect data, tradeoffs, and constraints are part of the job. You bring strong analytical depth, practical engineering discipline, and an appreciation for environments where reliability, trust, and operational impact matter more than novelty. Operational Context This role is primarily technical and office-based, with occasional interaction in operational environments depending on system needs.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
501-1,000 employees