Senior Data Scientist

Rockwell Automation is a global technology leader focused on helping the world’s manufacturers be more productive, sustainable, and agile. With more than 28,000 employees who make the world better every day, we know we have something special. Behind our customers - amazing companies that help feed the world, provide life-saving medicine on a global scale, and focus on clean water and green mobility - our people are energized problem solvers that take pride in how the work we do changes the world for the better. We welcome all makers, forward thinkers, and problem solvers who are looking for a place to do their best work. And if that’s you we would love to have you join us!

Job Description The Data Science & Innovation Organization is building the analytical engine that powers our AI product portfolio. As Senior Data Scientist, Agentic AI Products, you will own the data and modeling layer that our agentic systems depend on. This role sits directly alongside the Senior Agentic AI Engineer, who designs and deploys the reasoning, orchestration, and tool-use layers of our AI agents. Where that role builds the agent architecture, you build the empirical foundation. The empirical foundation consists of curated datasets, predictive models embedded as agent tools, statistical rigor for evaluation, and the feedback infrastructure that makes agents measurably better over time. Together, these two roles form the core of our applied AI capability.

Dataset creation & curation Build high-quality labeled datasets from operational data sources including structured databases, event logs, sensor streams, and document repositories Define feature engineering strategies for time-series, event-based, and unstructured data Predictive model development Build, validate, and maintain predictive models (e.g. anomaly detection, classification, forecasting) that serve as callable tools within agentic AI systems Apply rigorous statistical methods: hypothesis testing, cross-validation, and confidence interval estimation to ensure model outputs are trustworthy when surfaced by an agent Agent data interfaces & RAG grounding Own the data pipeline that populates structured knowledge bases used for retrieval-augmented generation in agentic products Build evaluation frameworks to measure retrieval quality and factual accuracy against domain specific ground-truth datasets Experimentation & statistical rigor Apply relevant causal inference techniques (e.g. synthetic controls, difference-in-difference) to isolate causal effects in operational environments Serve as the statistical conscience of the AI team: design measurement frameworks before shipping, and build internal culture around responsible AI performance claims Cross-functional enablement Collaborate with product managers to translate domain use cases into well-formed ML problem statements Work with AI engineers and data platform teams to align on feature store standards and machine learning best practices that support reliable agent tool integration The Essentials - You Will Have: Bachelor's Degree in relevant field. Legal authorization to work in the US is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening. The Preferred - You Might Also Have: Core data science foundations • 5+ years building end-to-end predictive models in production: from raw data through feature engineering, model training, evaluation, and deployment • Applied statistics: hypothesis testing, Bayesian methods, time-series modeling, uncertainty quantification, and understanding of common ML evaluation failure modes • Proficiency in Python (pandas, scikit-learn, PyTorch or equivalent); advanced SQL; familiarity with cloud data platforms (AWS, GCP, or Azure) AI agent and RAG data experience • Direct experience building datasets and evaluation pipelines for conversational AI, chatbot, or agent systems • Understanding of how