Senior Data Scientist - Artificial Intelligence R&d

Caterpillar Caterpillar · Industrial · Chicago, IL +1

Senior Data Scientist role focused on applied AI R&D within an industrial enterprise. Responsibilities include designing, building, and evaluating AI systems such as generative AI, LLMs, multimodal intelligence, RAG, and autonomous agents. The role involves full model lifecycle experimentation, fine-tuning models, optimizing retrieval systems and agentic workflows, leading data preparation, instrumenting for observability, translating research to prototypes, evaluating new AI capabilities, and mentoring junior scientists. Requires strong Python, ML, and data engineering skills, with experience in model evaluation and systems thinking.

What you'd actually do

  1. Design and execute AI experiments across the full model lifecycle: hypothesis formulation, data preparation, model development, evaluation, and iteration, maintaining research rigor in an ambiguous, fast-moving environment.
  2. Develop, fine-tune, and benchmark LLMs and multimodal AI models (text, vision, speech), including systematic evaluation of quality, latency, cost, and safety tradeoffs across model variants and providers.
  3. Explore and optimize knowledge retrieval systems (RAG pipelines, vector databases, hybrid search) and agentic workflows, ensuring relevance, accuracy, and scalability for enterprise use cases.
  4. Lead data preparation workstreams for model training, fine-tuning, and validation, including dataset curation, labeling strategy, synthetic data generation, and quality assurance.
  5. Instrument AI systems for observability and reproducibility using experiment tracking frameworks (e.g., Langfuse, MLflow), maintaining clear documentation of model versions, evaluation datasets, and performance baselines.

Skills

Required

  • Applied Statistics & Quantitative Methods
  • Analytical Rigor & Attention to Detail
  • Advanced Machine Learning & AI
  • Model Evaluation & Optimization
  • Programming Expertise (Python)
  • Data Engineering & Access
  • Requirements & Systems Thinking

Nice to have

  • Bachelor’s, Master’s, or PhD degree in Data Science, Computer Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a closely related technical field.
  • Proven 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.

What the JD emphasized

  • production intent
  • production-ready prototypes
  • production-oriented code

Other signals

  • applied AI
  • generative AI
  • LLMs
  • multimodal intelligence
  • RAG
  • autonomous agents
  • POCs with production intent
  • research opportunities