Senior Manager, Data Science – Digital Transformation, Manufacturing

Stellenbeschreibung:

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.