Senior Data Scientist – Agentic AI Products

Rockwell Automation

Senior Data Scientist responsible for building datasets and predictive models for AI products at Rockwell Automation. Collaborating with AI engineers to improve agent tools for client solutions.

Posted 4/25/2026full-timeRemote • Minnesota, Ohio, Wisconsin • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPandasPythonPyTorchScikit-LearnSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health Insurance including Medical, Dental and Vision
401k
Paid Time off
Parental and Caregiver Leave
Flexible Work Schedule

ATS Keywords

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Hard Skills & Tools

dataset creationfeature engineeringpredictive model developmentanomaly detectionclassificationforecastinghypothesis testingBayesian methodstime-series modelinguncertainty quantification

Soft Skills

collaborationcross-functional enablementmeasurement framework designresponsible AI performance claims

Certifications

Bachelor's Degree in Relevant Field