The Data Scientist applies advanced mathematical, statistical, and machine learning methods to operational problems drawn from freight and transportation networks—routing, network structure, utilization, demand and cost modeling, and prediction. This is an analytical role, not a data engineering or MLOps role; the focus is on formalizing ambiguous business questions, selecting and defending appropriate methods, and communicating findings and their limitations to operational and executive stakeholders. Graph-structured data is central to how the business is modeled, so comfort reasoning about networks is a distinct advantage. Responsibilities: ​Translate ambiguous operational problems into well-posed statistical, mathematical, or graph-theoretic questions Design and run analyses including statistical modeling, regression, optimization, simulation, and predictive/ML modeling Build and validate predictive models, and clearly characterize where they generalize and where they do not Work directly with real data—cleaning, exploring, and interrogating it before modeling Communicate methods and results to operational and executive stakeholders, including the reasoning behind methodological choices and their limitations Distinguish correlation from causation and signal from artifact, and state clearly when the data cannot answer the question Qualifications: STEM degree required; Master's or PhD in a quantitative field (statistics, applied math, operations research, physics, computer science, or similar) strongly preferred 4+ years of applied analytical or data science experience (mid–senior) Strong foundation in statistics, probability, and applied mathematics (linear algebra, optimization, or similar) Experience with predictive modeling and machine learning, and the judgment to know when a simpler method is the right one Proficiency scripting in Python or R for analysis—clean, correct code, without an expectation of shipping production systems Fluency with data manipulation (SQL, pandas/dplyr, or equivalent) Ability to reason about uncertainty and clearly state assumptions and their consequences Demonstrated ability to explain technical work to non-technical audiences Graph/network analysis (community detection, flow, shortest-path, centrality) or graph-based ML Time series, causal inference, or experimental design Optimization or operations research applied to logistics, routing, or scheduling Domain exposure in trucking, freight, logistics, or supply chain This job is classified under NOC Code: 21211 Why Bison:

• Thrive in a supportive team that provides coaching and training to help develop your skills and progress your career
• Dispersed work environments that promote a healthy work-life balance
• Meaningful and impactful work and projects with an essential service provider
• Join our engaging Wellness Program & extracurricular sports teams

About Bison:

• Celebrating over 50 years in Business
• Active in giving back through Corporate Social Responsibility and Charitable Giving
• Committed to environmental sustainability

Bison Transport is committed to Diversity and Inclusion in the Workplace.