The Data Analyst is a question-first, shared analytical resource supporting all of Bison's lines of business — Asset, Logistics, Intermodal, and LTL — across our North American operations. The role takes ambiguous commercial and operational questions — why a lane is losing money, where empty mileage is concentrating, what has changed in a customer's freight — and turns them into defensible, data-backed answers using our governed data environment.
This role is distinct from recurring report development and from statistical modelling. Your main purpose will be to translate a vague business question into a scoped analysis and a clear answer. Priorities shift between lines of business month to month, so breadth and the ability to get up to speed quickly on an unfamiliar corner of the business are central to the role. Outputs inform decisions made by operations, commercial, and senior leadership.
Specific Responsibilities include:
- Translate open-ended questions from operations, commercial, and leadership into scoped, answerable analyses without requiring the question to be fully formed.
- Write and validate SQL against a governed data warehouse to pull and join the data an analysis requires.
- Use Python for analysis, transformation, and investigative work beyond what SQL alone supports.
- Build focused Power BI views where a visual answer serves the audience better than a written one.
- Support any of Bison's lines of business as priorities dictate, ramping quickly on unfamiliar economics and operational context.
- Surface data-quality issues explicitly rather than analyzing around them.
- Scope claims to what the data supports, using ranges over false precision and stating caveats where warranted.
- Communicate findings clearly to non-technical commercial and operational audiences.
Our Ideal Candidate will possess:
- 2-5 years in an analytics or data role producing decision-grade work.
- Strong SQL, including multi-table joins and validating data of uncertain quality.
- Working Python for data analysis (pandas or equivalent), including scripting one-off investigations.
- Sufficient Power BI to build and maintain a clean, accurate view.
- Analytical judgment: distinguishing a rate problem from a mix problem from a data artifact, and stating where certainty is limited.
- Willingness to learn the distinct economics of each line of business — Asset, Logistics, Intermodal, and LTL.
- Clear written communication for a commercial and operational audience.
- 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