Lead Data Scientist - Artificial Intelligence R&D

Caterpillar Inc.•Peoria, IL

1d•Onsite

About The Position

The AI Research & Development (AI R&D) team at Cat Digital is seeking a Lead Data Scientist to serve as a senior technical leader at the intersection of applied AI, advanced technology, and forward‑looking research. This role focuses on designing, building, evaluating, and scaling AI prototypes and Proofs of Concept (POCs) with clear production intent, while also contributing to longer‑horizon research initiatives. The Lead Data Scientist will work hands‑on across the full AI lifecycle, from data preparation and experimentation to model evaluation and production‑readiness, collaborating closely with Product, Engineering, and Business stakeholders to translate emerging AI capabilities into impactful enterprise solutions.

Requirements

Demonstrated experience conducting structured technology research and literature reviews in advanced AI domains, with the ability to translate theoretical innovation into applied, enterprise‑grade solutions.
Extensive experience applying statistical thinking to experimentation, evaluation, and decision‑making in ambiguous, research‑driven environments.
Proven ability to design precise experiments, validate assumptions, and ensure accuracy and reproducibility of results.
Extensive knowledge of modern ML techniques, including deep learning, generative AI, NLP, computer vision, and multimodal systems, with hands‑on implementation experience.
Strong experience evaluating model quality and system‑level tradeoffs across accuracy, latency, cost, and scalability dimensions.
Extensive proficiency in Python for AI and ML development, including use of modern AI frameworks and tooling.
Strong understanding of data storage, retrieval, and processing systems required to support large‑scale training and experimentation workflows.
Ability to define technical and non‑functional requirements that bridge research, engineering, and production concerns.

Nice To Haves

Master’s, or PhD degree in Data Science, Computer Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a closely related technical field.
Extensive experience building and deploying advanced ML models beyond traditional analytics use cases.
Extensive proficiency in Python (NumPy, Pandas, PyTorch, LangChain, etc.); ability to write clean, maintainable, production-oriented code and contribute to shared AI infrastructure.
Strong hands‑on experience with generative AI, large language models, deep neural networks, and modern ML frameworks.
Demonstrated experience designing evaluation frameworks and benchmarks for AI systems.
Familiarity with AI infrastructure, cloud platforms (AWS, Azure), and scalable experimentation environments.
Advanced experience with version control, experiment tracking, and collaborative development (e.g., Git‑based workflows).
Experience working in Agile, cross‑functional product development environments.
Prior exposure to industrial, manufacturing, heavy equipment, or complex physical systems is a strong plus, but not required.

Responsibilities

Stay current with emerging AI research by conducting ongoing literature reviews across core AI workstreams such as speech/voice, vision, multimodal systems, retrieval, and autonomous agents, and actively apply relevant innovations to Cat Digital’s AI R&D portfolio.
Assess and compare academic and industry advancements to guide architecture choices, experimentation strategy, and production‑readiness decisions.
Synthesize research findings into practical, enterprise‑relevant insights that influence prototyping priorities and long‑term AI strategy.
Lead hands‑on experimentation and development of advanced machine learning and generative AI solutions, including LLMs, multimodal models (text, vision, speech), retrieval‑augmented generation (RAG), agents, and simulation/digital‑twin use cases.
Design, build, and curate high‑quality datasets for training, fine‑tuning, validation, and evaluation of AI models at scale.
Define and execute rigorous model evaluation strategies, including benchmarking model quality, accuracy, latency, cost, robustness, and safety tradeoffs.
Drive rapid prototyping and POC development with a strong focus on reproducibility, experiment tracking, and observability to enable informed technical decision‑making.
Research, compare, and optimize model architectures, algorithms, and AI system designs to improve performance, scalability, and enterprise readiness.
Partner with Product and Engineering teams to translate research outcomes and prototypes into production‑ready capabilities, including defining technical requirements and success metrics.
Communicate complex technical findings and insights clearly to both technical and non‑technical stakeholders, influencing roadmap and investment decisions.