• Lead and scale the Analytics and Data Science organization, hiring and developing managers and ICs while designing team structure to support deeper data science adoption across the business • Embed analytics and data science into core business workflows, ensuring insights, models, and decisioning systems are consistently used in product, risk, and growth decisions • Partner with Data Engineering to shape BI and data science platform architecture, influencing data models, tooling, and workflows to enable reliable, scalable analytics and ML • Translate business needs into analytical and data science solutions, working cross-functionally to ensure systems are reusable, well-governed, and aligned with platform constraints • Drive delivery of complex, cross-functional data initiatives, providing clarity in ambiguous problem spaces and ensuring outcomes meet business and technical expectations • Set technical and analytical standards across analytics and data science, raising the bar for modeling quality, productionization, and documentation through guidance and review

• Responsible for the creation of comprehensive data-driven agentic solutions • Collaborate across teams to establish best practices and reusable components • Actively participate in the development and deployment of machine learning, AI solutions • Identifies LLMs, programming languages, and tools that can bring efficiencies

• Lead the development and execution of data science projects, including problem formulation, data collection, analysis, modeling, and deployment of machine learning models. • Collaborate with cross-functional teams to identify business opportunities and translate them into data science projects. • Develop and implement machine learning algorithms and statistical models to solve complex business problems. • Perform exploratory data analysis to gain insights and identify patterns in large and complex datasets. • Work with data engineers to build robust and scalable data pipelines. • Evaluate and validate models using appropriate performance metrics and statistical techniques. • Present findings and insights to stakeholders in a clear and concise manner. • Stay up-to-date with the latest advancements and best practices in data science and machine learning. • Mentor and guide more junior data scientists in the team.

• Build machine-learning systems that power some of the most advanced logistics intelligence products in the industry • Analyze large, noisy datasets from cameras, OCR, detections, and geospatial pipelines to uncover actionable patterns • Design and evaluate algorithms for truck re-identification, geospatial clustering, equipment classification, OCR text labeling, anomaly detection, and more • Collaborate with engineering and data engineering teams to scale models from prototype to production • Work closely with product teams to deeply understand customer needs and translate them into modeling and analytics initiatives • Apply scientific thinking to continuously test, iterate, and refine approaches as new data becomes available