Senior Data Scientist - Agentic AI products

Rockwell Automation•Mayfield Heights, MN

1d•Remote

About The Position

The Data Science & Innovation Organization is building the analytical engine that powers Rockwell Automation's AI product portfolio. As a 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: 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.

Requirements

Bachelor's Degree in Relevant Field.
Legal authorization to work in the U.S. We will not sponsor individuals for employment visas, now or in the future, for this job opening.
Typically requires a minimum of 8 of relevant professional experience, with a focus on AI/ML Engineering and Agentic AI product development.
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.
Strong 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 & RAG data experience.
Direct experience building datasets and evaluation pipelines for conversational AI, chatbot, or agent systems.
Understanding of how predictive model outputs (scores, probabilities, confidence intervals) need to be structured to be safely consumed as agent tool responses.

Responsibilities

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.
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.
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.
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.
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.