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Senior Data Scientist

Rockwell Automation · Milwaukee, WI · Full time

Posted Jul 28, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a model you built that changed a business decision.

What they are checking: Whether they have delivered real impact, not just accurate models in a notebook.

How do you explain model limits to product leaders who want to launch quickly?

What they are checking: Whether they communicate uncertainty and risk clearly without blocking progress.

What would you do

How would you divide work with the Senior Agentic AI Engineer so nothing falls through the cracks?

What they are checking: Whether they understand the split between agent architecture and the empirical foundation.

An agent's offline metrics look great but users say it's not helpful. What do you do?

What they are checking: Whether they question whether metrics reflect real value and investigate the gap.

What would make you decide a predictive model shouldn't be used inside an agent at all?

What they are checking: Whether they know when a model isn't reliable enough for automated use.

About the work

How would you design an evaluation framework for an AI agent so we can tell if it's actually getting better?

What they are checking: Whether they can bring statistical rigor to agent evaluation, which the posting makes central.

Walk me through how you'd build a predictive model that an agent calls as a tool.

What they are checking: Whether they understand how to package models with clear inputs, outputs and uncertainty for agent use.

How do you curate a dataset for training or evaluating an agent when industrial data is messy and sparse?

What they are checking: Whether they can handle real manufacturing data quality issues instead of assuming clean inputs.

How do you design feedback loops so agent performance improves from real usage?

What they are checking: Whether they can build the feedback infrastructure the posting names as part of the role.

How do you tell whether a change in agent performance is real or just noise?

What they are checking: Whether they apply sound statistical testing to decide what improvements to trust.

Practice this interview out loud.

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The full job description

As published by Rockwell Automation.

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.

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