Job description

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 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. Primary Responsibilities: 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 predictive model outputs (scores, probabilitie What We Offer: Health Insurance including Medical, Dental and Vision 401k Paid Time off Parental and Caregiver Leave Flexible Work Schedule where you will work with your manager to enjoy a work schedule that can be flexible with your personal life. To learn more about our benefits package, please visit at www.raquickfind.com . This position is part of a job family. Experience will be the determining factor for position level and compensation. At Rockwell Automation we are dedicated to building a diverse, inclusive and authentic workplace, so if you re excited about this role but your experience doesn t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right person for this or other roles. #LI-Hybrid #LI-LifeAtRok #LI-SB1 We are an Equal Opportunity Employer including disability and veterans. If you are an individual with a disability and you need assistance or a reasonable accommodation during the application process, please contact our services team at 1 (844) 404-7247. Rockwell Automation’s hybrid policy aligns that employees are expected to work at a Rockwell location at least Mondays, Tuesdays, and Thursdays unless they have a business obligation out of the office.

Requirements

Must-haves

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!
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
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.
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

Nice-to-haves

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Application prep

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Keywords to mirror

agileaimlmachine learningpythonsqlawsgcpazure

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401k

Paid Time Off