Lead Data Scientist

Career Area:

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.

Cat Digital is the digital and technology arm of Caterpillar Inc., responsible for bringing world class digital capabilities to our products and services. With almost one million connected assets worldwide, we're focused on using IoT and other data, technology, advanced analytics and AI capabilities to help our customers build a better world.

Build the Digital Backbone of Modern Manufacturing

We’re assembling a dynamic team to develop and scale our Manufacturing & Supply Digital Platform—a next-generation software framework that transforms how manufacturing and supply operations connect, collaborate, and optimize.

This platform is not an ERP system. It’s a purpose-built digital layer that integrates data, processes, and resources across the entire manufacturing lifecycle—from design and engineering to production and distribution.

This initiative is powered by NVIDIA technologies, including the Omniverse platform and AI computing capabilities, enabling immersive digital twins, accelerated simulation, and intelligent automation. You’ll be part of a team that’s not just building software—but shaping the future of how manufacturing works through AI-driven, collaborative, and scalable digital solutions.

As part of this initiative, you’ll contribute to:

• System Integration: Seamlessly connecting diverse manufacturing and supply systems, data sources, and workflows into a unified digital ecosystem.
• Data-Driven Decision Making: Harnessing real-time data collection, analysis, and visualization to deliver actionable insights and operational intelligence.
• Automation & Optimization: Driving efficiency through intelligent scheduling, predictive maintenance, and quality control—without replacing core transactional systems.
• Enhanced Collaboration: Enabling transparent communication and coordination across teams, functions, and geographies.

If you're passionate about digital platforms, industrial innovation, and working with cutting-edge technologies—this is your opportunity to make a meaningful impact.

Key Responsibilities:

• Coach and mentor Junior Data Scientists in the creation, validation, and application of statistical, machine learning and LLM models as well as in implementing AI solutions
• Enhance team’s creativity to solve business problems using machine learning techniques
• Evaluate and implement modern optimization algorithms: stochastic search, evolutionary/genetic, reinforcement, and multi-objective optimization.
• Lead expert-level validation of AI/ML models using industrial analytics metrics such as yield improvement, downtime reduction, and prediction accuracy.
• Establish standards for data/model lifecycle management, production monitoring, and feedback from physical operations.

Required Skills:

• Deep expertise in machine learning and deep learning techniques — designs composable model services (ensembles, retrieval augmented generation, multimodal orchestration), establishes rigorous evaluation methods.
• Demonstrated experience in leveraging classical optimization (branch & bound, heuristics, integer programming) techniques.
• Advanced Python knowledge to handle high-performance analytical workloads, simulation and deployment.
• Deep knowledge of manufacturing process analytics: multivariate analysis, defect analytics.
• Team/project leadership with publications or patents in industrial ML or optimization.
• Experience in building analytics on top of OpenUSD-powered asset graphs, integrating real/virtual process data for digital twins.

Nice to Have Skills:

• Hands-on with cloud-native deployments, industrial IoT/edge analytics, and data pipeline automation.
• Understanding of domains across plant/line telemetry, quality, maintenance, logistics, or scheduling—can translate noisy signals into predictive and prescriptive use cases

Educational Background: Typically requires a Bachelor’s degree, preferably in computer science, Artificial Intelligence, Data Science, mathematics, or a similar field with quantitative coursework, and 8-12 years of professional experience in associated field is required, a Master’s degree and 6-8 years of experience, or a PhD and 2-5 years of experience in relevant field.

Posting Dates:

Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to apply

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