Senior Manager, Data Science- Digital Transformation, Manufacturing

Rockwell Automation•Orlando, FL

2d•Remote

About The Position

The Senior Manager, Data Science leads the delivery, growth, and technical direction of applied data science engagements for industrial and manufacturing clients. This is a dual-mandate role to own delivery and commercial outcomes, while developing our team of data scientists, consultants, and engineers. This remote role sits at the intersection of physical systems and modern AI. The goal is to translate plant-floor and enterprise manufacturing problems into production-grade models. These models include predictive quality, process optimization, model predictive control, digital twin, industrial data operations, and predictive maintenance. The clients operate and sustain these models after we leave. We're delivering solutions on Rockwell's Autonomy stack and best-in-class partner technology.

Requirements

Bachelor's Degree in Relevant Field.
Legal authorization to work in the U.S. We will not sponsor individuals for employment visas, now or in the future, for this job opening.
The ability to travel to client sites.

Nice To Haves

Advanced degree (Masters/PhD) in a quantitative discipline.
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.

Responsibilities

Manage delivery across a portfolio of data science engagements including scope, staffing, schedule, quality, margin, and risk.
Be the senior authority on engagements by consulting on use case selection, setting modeling approach, reviewing architecture and results, and leading delivery expansion.
Translate client challenges into ML/AI use cases; lead proof-of-concept work and own the path from pilot to scaled production deployment.
Manage delivery escalations and course corrections; enforce objective, measurable acceptance criteria on every deliverable.
Be a trusted advisor to operations, engineering, and C-suite stakeholders; build and expand executive relationships across priority manufacturing accounts.
Lead customer co-innovation opportunities.
Lead pursuit and proposal work: shape opportunities, author scope and estimates, and author or review SOWs and change orders with defensible scope boundaries and commercial protection.
Carry a sales-support/origination target in partnership with sales/account leadership; convert delivery credibility into follow-on and expansion work.
Lead delivery teams
Mentor and train our staff
Develop services and software offerings
Build reusable assets, accelerators, and methods that reduce delivery cost and raise consistency across engagements.
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.