Data Scientist role focused on transforming data into advanced analytical and predictive insights for decision-making within Finance. This role involves data exploration, statistical and machine learning modeling, data preparation, and translating analytical outputs into actionable insights. It supports automation and advanced analytics enablement, model monitoring, and business partnership, working closely with IT and Data Platforms.

Data Scientist role at Caterpillar focused on performing analytical tasks and initiatives on large datasets to support data-driven business decisions and enable the Packaging Analytics process. Responsibilities include data gathering, processing, exploring ML techniques like NLP and text analysis, defining analysis scope, reporting insights, and conducting research on data model optimization and algorithms.

This role focuses on smart manufacturing engineering within Caterpillar, involving process design, manufacturing feasibility assessment, product manufacturability analysis, tooling design, process optimization, and documentation. It also includes managing production data, on-site process management, problem-solving, and exploring AI applications in manufacturing for tasks like defect identification and process monitoring. The role requires Python for data processing and automation, basic PLC programming, and support for automation and robotics integration. Experience with MES, data acquisition systems, and a basic understanding of AI/ML concepts are preferred.

This role focuses on driving enterprise-wide digitalization projects, leveraging advanced data analytics and intelligent office automation. It involves developing predictive models, integrating data, and implementing AI-driven solutions for office workflows, including research into enterprise knowledge bases and generative AI for information retrieval and Q&A. The role also supports the deployment of Caterpillar's standard systems.

This role is for a Software Engineer focused on intelligent manufacturing at Caterpillar's Tianjin facility. The engineer will develop and implement extensions and integrations for production systems (ERP, MES), participate in IoT projects (data acquisition, integration), and assist with system design and documentation. A key aspect is exploring and applying machine learning, AI, or data analysis methods to manufacturing scenarios like quality analysis, anomaly detection, and predictive analytics. The role also involves supporting existing systems, project management assistance, and staying updated on new technologies in intelligent manufacturing and AI.