Data Scientist

The Data Scientist applies advanced mathematical, statistical, and machine learning methods to operational problems drawn from freight and transportation networks—including routing, network structure, utilization, demand and cost modeling, and prediction. This analytical role focuses on formalizing ambiguous business questions, selecting appropriate methods, and communicating findings and their limitations to operational and executive stakeholders. Graph-structured data are central to the business model, so comfort reasoning about networks is an 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, clearly characterizing 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 Ph.D. 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.

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