Senior Manager, Data Science – Digital Transformation, Manufacturing

• Lead the delivery, growth, and technical direction of applied data science engagements for industrial and manufacturing clients
• Own delivery and commercial outcomes while developing data scientists, consultants, and engineers
• Translate plant-floor and enterprise manufacturing problems into production-grade models
• Manage delivery across a portfolio of data science engagements, including scope, staffing, schedule, quality, margin, and risk
• Serve as senior authority on engagements by advising on use-case selection, modeling approach, architecture, and results
• Lead proof-of-concept work and drive the path from pilot to scaled production deployment
• Manage delivery escalations and course corrections; enforce measurable acceptance criteria
• Build and expand executive relationships across manufacturing accounts
• Lead customer co-innovation opportunities
• Lead pursuit and proposal work, including opportunity shaping, scope and estimates, SOWs, and change orders
• Support sales and account leadership with origination and expansion work
• Lead delivery teams; mentor and train staff
• Develop services, software offerings, reusable assets, accelerators, and methods
• Grow the practice's applied AI capability in industrial contexts
• Work with internal groups and ecosystem partners to shape solutions and go-to-market offerings

Requirements

• Bachelor's Degree in Relevant Field
• Legal authorization to work in the U.S.; Rockwell Automation will not sponsor individuals for employment visas, now or in the future, for this job opening
• Ability to travel to client sites
• Advanced degree (Masters/PhD) in a quantitative discipline (preferred)
• Typically requires 12+ years of professional experience, including 5+ years in a delivery role
• 5+ years in a client-facing consulting, professional services, or internal-consulting capacity, including ownership of engagement scope, budget, and margin
• Demonstrated delivery in a manufacturing, industrial, energy, or process-intensive environment
• Hands-on depth in the modern ML stack; production experience with time-series, forecasting, optimization, or control-adjacent modeling
• Experience deploying models into sustained operation (MLOps, monitoring, retraining, and handover to client teams)
• Ability to translate technical outputs into executive-ready recommendations and defend them under scrutiny
• Direct experience with industrial data operations, model predictive control, advanced process control, or digital twin implementations
• Familiarity with industrial data infrastructure: historians (PI, FactoryTalk), MES/SCADA, ISA-95 context, edge/cloud data pipelines
• Prior experience in consulting or a comparable industrial-AI practice
• Experience building and scaling a team from a small base

Core Competencies

Demonstrates expertise in leading applied data science engagements within industrial and manufacturing sectors, focusing on model development, client relationship management, and team mentorship. Proficient in translating complex technical outputs into actionable insights for executive stakeholders while ensuring successful project delivery and operational excellence.

Highest-signal resume keywords

• Applied Data Science Leadership
• Client-Facing Consulting Experience
• MLOps Deployment Experience
• Industrial Data Operations Expertise
• Team Building and Mentorship

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Time-Series Modeling
• Forecasting
• Optimization
• Model Predictive Control
• Advanced Process Control
• Digital Twin Implementations
• Production-Grade Model Development
• Data Science Engagement Management
• Quantitative Analysis

Soft Skills

• Executive Communication
• Mentoring
• Problem Solving
• Relationship Building
• Adaptability

Certifications & Qualifications

• Bachelor's Degree in Relevant Field
• Advanced Degree (Masters/PhD) in Quantitative Discipline (Preferred)

Industry Keywords

• Manufacturing
• Industrial
• Energy
• Process-Intensive Environment
• Consulting

Tools & Technologies

• MLOps
• PI Historians
• FactoryTalk
• MES/SCADA
• Edge/Cloud Data Pipelines