• 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

• Lead machine learning teams to production grade solutions, ensure quality of delivery and robustness of the solution, and drive project delivery timelines • 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

• Lead the end-to-end development of machine learning and statistical models for traffic prediction, route optimization, congestion analysis, and mobility analytics • Design and implement scalable data pipelines and workflows for processing large-scale geospatial and temporal datasets • Analyze traffic patterns using geospatial data and sensor feeds (GPS, IoT, traffic cameras, etc.) to derive actionable insights • Collaborate with engineering teams to integrate machine learning models into production systems • Conduct exploratory data analysis (EDA), feature engineering, and model evaluation to ensure robust and accurate predictions • Stay current with the latest advancements in machine learning, deep learning, geospatial analytics, and big data technologies • Mentor junior data scientists and provide guidance on best practices for model development and data analysis • Present findings and insights to cross-functional teams, stakeholders, and clients in a clear and actionable manner.