Lead Data Scientist

Career Area: Technology, Digital and Data Job Description: Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other.  We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. Job Summary Provides technical leadership in applying Data Science, AI, and Machine Learning to transform large-scale data into actionable insights, intelligent automation, and business value across Packaging and related enterprise functions.   What You Will Do Lead the definition of business requirements, analytical scope, and solution architecture; translate business needs into scalable technical solutions. Collaborate with stakeholders, conduct workshops, and communicate actionable insights through dashboards, visualizations, and executive presentations. Lead the design, development, deployment, and optimization of AI/ML, deep learning, computer vision, and generative AI solutions to address complex packaging, supply chain, logistics, and engineering challenges. Lead large-scale data gathering, data mining, feature engineering, and data processing activities; create scalable data models and pipelines. Explore, promote, and implement AI-driven capabilities using LLMs, agentic AI frameworks, NLP, semantic search, and advanced analytics techniques. Drive the development and deployment of predictive, optimization, quality, sustainability, and automation solutions using machine learning and data science methodologies. Establish MLOps, model governance, monitoring, retraining, and continuous improvement processes to support reliable production deployment of AI solutions. Research and evaluate emerging AI technologies, algorithms, and frameworks to improve solution effectiveness and business impact.   What You Have Business Partnership & Requirements Analysis Knowledge of business analysis techniques and stakeholder engagement practices; ability to translate business needs into scalable data science and AI solutions. Level: Extensive Experience Engages with business leaders, clients, and stakeholders to understand strategic priorities. Leads workshops, requirement-gathering sessions, and solution discovery activities. Defines analytical scope, success criteria, and technical requirements for AI initiatives. Translates complex business challenges into data science, machine learning, and automation solutions. Effectively communicates technical concepts to executive, technical, and business audiences. Partners with cross-functional teams to drive adoption and business value realization.   Query & Database Access Tools Knowledge of data management systems and data access technologies; ability to retrieve, transform, and optimize enterprise data for analytics and AI applications. Level: Extensive Experience Writes, optimizes, and supports complex SQL queries across multiple databases and data sources. Works extensively with structured and unstructured data environments. Designs data retrieval and transformation strategies supporting AI and analytics workloads. Consults on query optimization, performance tuning, and database best practices. Utilizes big data technologies and distributed data processing frameworks. Evaluates database technologies and architectures supporting AI initiatives.   Data Analysis & Statistical Modeling Knowledge of statistical methods, predictive analytics, and data-driven decision-making; ability to transform data into meaningful business insights. Level: Working Knowledge Performs advanced statistical analysis, predictive modeling, and machine learning experimentation. Uses statistical techniques to identify patterns, trends, anomalies, and business opportunities. Translates complex analytical findings into actionable business recommendations. Develops metrics, KPIs, and analytical frameworks to support strategic decisions. Evaluates model accuracy, effectiveness, and business impact using statistical methodologies. Communicates analytical insights to both technical and non-technical stakeholders.   Artificial Intelligence & Machine Learning Knowledge of machine learning, deep learning, generative AI, computer vision, and agentic frameworks; ability to develop, deploy, and manage AI-based solutions that drive business outcomes. Level: Working Knowledge Leads the deployment of machine learning, deep learning, computer vision, and generative AI solutions. Develops and implements LLM-based applications using agentic AI, NLP, embeddings, summarization, and semantic search technologies. Selects, trains, evaluates, and optimizes models using TensorFlow, PyTorch, Scikit-Learn, PySpark MLlib, and related frameworks. Monitors model performance and implements retraining, scalability, and error-handling strategies. Coaches and mentors teams on AI technologies, methodologies, and best practices. Applies AI solutions to solve complex packaging, logistics, engineering, and supply chain business challenges.   Programming Languages & Software Development Knowledge of programming concepts, software development practices, and application development frameworks; ability to build scalable AI-enabled applications and enterprise solutions. Level: Working Knowledge Demonstrates expertise in Python and SQL. Develops scalable AI applications using Streamlit, Gradio, and cloud-native architectures. Integrates AI services with enterprise business systems and backend platforms. Guides teams in selecting development tools, frameworks, and coding standards. Oversees development activities to ensure quality, maintainability, and performance.   Cloud & Data Engineering Knowledge of cloud platforms, data engineering practices, and enterprise-scale distributed systems; ability to design and implement scalable AI and analytics solutions. Level: Working Knowledge Works with relational and non-relational databases, data warehouses, big data platforms, and caching technologies. Utilizes cloud-native architectures to support AI, analytics, and automation solutions. Evaluates emerging cloud technologies and recommends improvements.   MLOps & Production Deployment Knowledge of model lifecycle management, MLOps frameworks, and deployment architectures; ability to operationalize AI solutions at enterprise scale. Level: Working Knowledge Builds automated deployment, monitoring, governance, and model management solutions. Ensures the scalability, reliability, security, and maintainability of production AI systems. Develops monitoring strategies to track model drift, performance degradation, and operational issues. Drives continuous improvement of AI operations and deployment methodologies.   Domain Expertise – Packaging, Supply Chain & Logistics Knowledge of packaging engineering, supply chain, logistics, transportation, procurement, or manufacturing operations; ability to apply AI/ML technologies to business challenges in these areas. Level: Working Knowledge Applies AI/ML techniques across relevant enterprise domains. Understands operational workflows, business processes, and optimization opportunities within industrial environments. Develops AI-driven solutions for quality prediction, defect detection, optimization modeling, sustainability, and automation. Leverages domain expertise to accelerate solution adoption and business impact. Collaborates with engineering and business teams to identify high-value AI opportunities. Provides technical leadership on AI initiatives supporting Packaging and Supply Chain transformation.   Required Qualifications Bachelor’s or Master’s degree in Engineering or Computer Science; Data Science, Artificial Intelli