The posting
About the Team & Role
Auger is hiring a Data Scientist to help turn raw customer data into the structured understanding within our ontology that powers autonomous operations and execution. Russell Allgor’s supply chain science team builds advanced AI and optimization systems for some of the most complex supply chain problems in the world, and bringing that science into production requires a tight connection between the models and the data that feeds them. This role sits at that connection point.
You will work shoulder-to-shoulder with seasoned supply chain experts, learning the domain on the job while bringing the analytical horsepower to model it. Your core work will be to understand how a customer’s business actually operates, interpret the data their systems produce, and build the inference, regression, simulation, and optimization logic that closes the loop between business processes and the world model that represents them. You will deeply understand what the models need, why they need it, and how supply chain data behaves in practice, then translate that understanding into clear, actionable requirements for the engineers building the underlying infrastructure.
You will be a critical link between Auger’s applied science and data engineering teams. When data quality issues arise, you will help diagnose whether the issue is the source data, a pipeline problem, a modeling assumption, or a mismatch between the data and the real-world business process it represents. When a model moves toward production, you will own the quality validation to make sure it works in the real world.
This is a role built for potential. We care far more about how you think — how you question things that don’t make sense, break a hard problem into testable pieces, and use data to prove or disprove a hypothesis — than about the length of your résumé. If you are smart, curious, driven, and coachable, and you like to build things that work, you will thrive here.
This role is based in Bellevue, WA or Dallas, TX
What You’ll Do
Understand how customer businesses actually operate: how work flows, where decisions are made, and what good looks like operationally. Interpret customer data and assign context — figure out what the data means, how entities relate, and where the gaps and inconsistencies are. Form and test hypotheses using data to prove or disprove ideas about the system and the relationships between entities within it. Build inference techniques and regression models that extract signal and quantify relationships. Translate business logic and objectives into mathematical constraints and quantifiable calculations. Identify missing concepts needed to close process and data loops — spot what isn't there yet but needs to be. Serve as the critical link between applied science and data engineering: translate scientific requirements into engineering specifications and vice versa.
What You Bring
Master's degree in Data Science, Statistics, Applied Mathematics, or a related quantitative field; undergraduate degree in Engineering, Mathematics, Economics, or Computer Science. Strong proficiency in Python and SQL; comfortable working with large, messy, real-world datasets. Experience with machine learning and optimization models, with strong statistical intuition — you notice when results look wrong and can articulate why. Enough familiarity with data pipelines and infrastructure to have productive technical conversations with data engineers. Sharp analytical instincts paired with strong common sense: you can tell when something doesn't add up, and you use data to prove or disprove it. A builder's mindset — you break problems into testable components, take things apart, and improve them. Comfort with ambiguity and a bias toward asking the right question before assuming the right answer. Supply chain and logistics experience is ideal. Hands-on experience with any complex, interrelated physical system is highly beneficial. Candidates who demonstrate the smarts, drive, and curiosity described above are encouraged to apply — we hire for potential.
