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• Design, develop, and implement graph-based analytics solutions supporting fraud detection and investigative analysis. • Use graph databases and network analysis techniques to identify hidden relationships, patterns, and connections across entities. • Develop graph models representing individuals, organizations, transactions, accounts, programs, and other relevant entities. • Apply graph algorithms involving centrality, community detection, link analysis, path analysis, clustering, and anomaly detection. • Integrate graph analytics with machine learning, statistical analysis, and other advanced analytic methods. • Analyze structured, semi-structured, and unstructured data from public, non-public, and commercial sources. • Support entity resolution, identity matching, relationship mapping, and risk-scoring activities. • Develop and refine fraud-detection models, rules, and investigative use cases. • Collaborate with investigators and analysts to translate operational and investigative needs into graph analytics solutions. • Build visualizations, link charts, dashboards, and other work products that clearly communicate complex relationships. • Support the development, testing, validation, and deployment of graph analytics models and applications. • Evaluate model performance and recommend adjustments to improve accuracy, scalability, and usefulness. • Document methodologies, data sources, assumptions, model designs, findings, and limitations. • Participate in technical reviews, quality-control activities, and project demonstrations. • Present analytical findings and recommendations to technical and non-technical stakeholders. • Support the maintenance and improvement of deployed graph analytics solutions.

Role Description 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. - 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 Benefits - 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 Company Description - 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.

• Be able to lead a small clinical study or co-lead a larger study or studies with moderate guidance • Be able to support several clinical studies with minimal guidance • Manage and monitor the progress of data management activities with CROs or other vendors on assigned studies • Review protocols for appropriate data capture including electronic Case Report Form (eCRF) design. Be able to perform a thoroughly detailed review of eCRF data requirements. • Interact with CRAs, programmers, study managers and statisticians in designing eCRFs and creating the annotated eCRF and CRF completion guidelines. • Develop data edit check specifications and run data listings as required • Coordinate the design and testing of Electronic Data Capture (EDC) systems according to company standards. Able to resolve EDC system issues with team members • Develop the Data Management Plan for a clinical study. Able to review and provide feedback to the clinical team on other study documents e.g. Clinical Monitoring plans, Statistical analysis plans and vendor specifications • Enter data queries into the EDC system to be resolved at investigational sites and manage the resolution of those queries • Perform reconciliation of header data from external data sources against the clinical database • Perform Serious Adverse Event reconciliation activities according to SOPs and guidelines • Assist in database upgrades/migrations including performing User Acceptance Testing • Able to maintain study workbooks and data management files • Perform database lock and freeze activities per company SOPs • Participate in regular team meetings and provide input when appropriate • Provide input into the development of data management SOPs, Work Instructions, and process documents • Contribute to a professional working environment including the application of the client’s Code of Business Conduct and Ethics • Assist with the training of new employees and/or contractors