Data Scientist - Supply Chain

Summary

Auger is a company focused on advanced AI and optimization systems for supply chain problems. They are seeking a Data Scientist to connect customer data with business processes, building models and validating their quality in real-world applications.

Responsibilities

• 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

Skills

• 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

Qualifications

Must Haves

• 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

Nice to Haves

• Supply chain and logistics experience is ideal
• Hands-on experience with any complex, interrelated physical system is highly beneficial