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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Rockwell Automation, Inc. - **Location:** Milwaukee, WI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Databases, Data Files, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Software Product Management, Raw Data, Cloud Services, Unstructured Data, Feature Engineering, Chatbots, Sql Optimization, Pytorch, Retrieval-Augmented Generation, Pandas, Core Data, Scikit Learn, Virtual Agents, Data Pipelines - **Published:** July 29, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/87863666/1 ## About the Role * 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 ## 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 ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Intelligent Automation using Machine Learning](https://www.wearedevelopers.com/videos/157-intelligent-automation-using-machine-learning) - [Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata](https://www.wearedevelopers.com/videos/1616-bringing-clarity-to-event-streams-enabling-analytics-and-ai-through-rich-metadata) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)