MSC Intelligent Operations Manager

T-Mobile•Overland Park, KS

1d•$80,200 - $144,700•Onsite

About The Position

The Magenta Services Center (MSC) is T-Mobile's multi-tower Shared Services Organization (SSO) spanning HR, Finance, and Accounting operations. This Manager role exists to build and operate the digital foundation that powers how the MSC works, not as a technology project, but as a permanent operating discipline. As the MSC matures from successful migration into governed, scalable service delivery, the function ensures that technology intelligence is embedded into operations, not bolted on after the fact. The person who thrives here bridges two worlds: deep fluency with commercial AI platforms and automation tooling on one side, and the structured thinking of data governance, enterprise architecture, and service management on the other. You will operate at the intersection of IT partnership, Centers of Excellence alignment, and MSC operations, translating AI possibility into MSC practice. This is not a role for someone who experiments with AI as a hobby. It is for someone who can design the system by which an entire shared services organization operates with AI embedded as a default, not an option. The Magenta Services Center (MSC) has built its operational foundation and is working to unlock the use of AI as a core capability alongside with other Service Management practices. Across its five value streams, the MSC processes high-volume, structured, repeatable work, exactly the conditions where AI-enabled automation, decision support, and productivity will amplify the value of the organization. Without a dedicated function to manage MSC’s AI strategy, coordinate enablement with IT and CoE partners, maintain enterprise compliance, and drive individual productivity adoption, the MSC might just fall in consuming AI tools reactively and inconsistently, leaving significant capacity and quality opportunity on the table. This Manager closes that gap and builds the capability from which the MSC's next phase of maturity begins.

Requirements

Bachelor's Degree plus 3 years of related work experience OR advanced degree with 1 year of related work experience OR combination of education and experience deemed equivalent (Required)
4-6 years Experience in a GBS, Shared Services, or enterprise operations environment with direct involvement in digital transformation, AI enablement, or technology-driven process improvement (Required)
3+ years Hands-on proficiency with commercial AI platforms (GPT, Claude, or equivalent) in an operational or automation context, not limited to personal productivity (Required)
2+ years Designing or governing a data architecture, data domain structure, or analytics foundation in a multi-function or multi-process environment (Required)
2+ years Coordinating AI or digital enablement work across IT, business, and Centers of Excellence stakeholders, including intake, prioritization, and delivery management (Required)
2+ years Building and presenting value-benefits estimates or business cases for technology investments, with accountability for post-implementation outcome tracking (Required)
1+ years Hands-on experience designing or deploying agentic AI workflows. Experience working with AI CoE, IT, or platform engineering teams to define, test, and govern agentic deployments. Includes experience scoping appropriate use cases, documenting workflow logic, and establishing human oversight and fallback mechanisms. (Required)
Automation tooling beyond AI (RPA, workflow automation, integration platforms) in a finance or operations context (Required)
AI Data Requirements & Readiness - Able to identify and articulate what data AI systems, agentic workflows, and automation initiatives need to function: inputs, features, quality thresholds, pipeline dependencies. Translates those requirements into clear, actionable inputs for the Data Strategy, Reporting & Analytics team's roadmap. Does not design data architecture or own data domains. Assesses AI readiness gaps, surfaces them to the right owners, and tracks resolution. Understands data as an enabler of AI outcomes, and advocates for the AI perspective within data planning forums. (Required)
AI Governance & Compliance - Understands enterprise AI guardrail frameworks and can translate policy into MSC-level standards. Has working knowledge of data classification implications for AI tool use, including what can and cannot be fed into commercial models. (Required)
Technology Coordination - Experienced managing AI or digital initiatives that span IT, business operations, and Centers of Excellence. Can align stakeholders with different incentives, sequence dependencies, and maintain momentum without direct authority over delivery teams. (Required)
Value-Benefits Estimation - Applied structured thinking to estimate the cost, effort, risk, and expected return of technology initiatives.
Adoption Enablement - Has designed and executed AI or digital tool adoption programs for operational teams. Understands the difference between launching access to a tool and embedding it into daily practice. Builds pathways that make good behavior the path of least resistance. (Required)
GBS / SSO Delivery Knowledge - Understands how shared services operations work end-to-end: tiered delivery, value stream structure, service ownership, and applies that context when identifying and designing AI use cases. Does not treat AI as a standalone capability. (Required)
Independent Execution - Capable of defining structure, setting priorities, and delivering outcomes in an environment where the function is still being built. Self-directed, resourceful, and comfortable operating without a full team for extended periods. (Required)
Executive Communication - Translates technical AI and data concepts into clear, stakeholder-appropriate narratives. Comfortable presenting to senior leadership in a way that enables decisions, not just awareness.
Agentic AI & Workflow Design - Understands agentic AI architectures and can design multi-step autonomous workflows for operational use cases: defining trigger conditions, decision logic, action sequences, system interactions, and human-in-the-loop oversight checkpoints. Critically, understands where agentic deployment is appropriate and where it is not, particularly in processes with SOX, compliance, or financial control exposure. Coordinates all agentic workflow design and deployment with the AI CoE and IT to ensure enterprise architecture alignment, data security compliance, and auditability. (Required)
AI / ML Specialization or Certification - Demonstrates structured understanding of AI/ML concepts, capabilities, and limitations beyond tool familiarity (Required)
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

Acceptable areas of study include Information Systems, Computer Science, Data Science or related field (Preferred)
Direct experience with enterprise AI governance or acceptable use frameworks, including data classification compliance in an AI context (Preferred)
Telecom, large-scale enterprise, or highly regulated industry environment (Preferred)
Automation Architecture Awareness - Familiarity with how AI integrates with automation platforms (RPA, API-based workflows, integration middleware). Does not need to build system integrations, but must be able to design requirements and assess feasibility. (Preferred)
ITIL / Service Management Awareness - Familiarity with service management principles: Program Management, Continual Service Improvement, Change management, as they apply to embedding AI into operational service delivery. (Preferred)
ITIL Foundation (v3 or v4) - Supports integration of AI enablement into a service management governance context (Preferred)
Lean Six Sigma (any level) - Supports structured approach to process analysis and AI use case identification within continuous improvement frameworks (Preferred)
Data Governance or Analytics Certification - Demonstrates foundational knowledge of data management principles applicable to structuring data domains for AI readiness (e.g., DAMA CDMP, Coursera Data Governance, or equivalent) (Preferred)

Responsibilities

Serve as the MSC's formal liaison to T-Mobile's AI Center of Excellence (CoE) and IT functions for all AI enablement initiatives across value streams. This is not an informal relationship, this role owns the intake, alignment, and governance checkpoint for every AI initiative touching MSC operations, including agentic workflow deployments, commercial tool adoption, and platform integrations. Ensures proper coordination in deployment of AI within an established CoE and IT coordination model. Maintain a structured portfolio of MSC AI initiatives, track dependencies with CoE and IT, ensure sequencing is governed, and represent the MSC's priorities within enterprise AI planning forums.
Ensure that all AI usage within the MSC, whether system-level automation or individual productivity tooling, adheres to T-Mobile's enterprise AI guardrails, data classification standards, and acceptable use policies. Act as the MSC's governance checkpoint for new AI tool adoption, maintaining a coherent and compliant approach across teams. Translate enterprise policy into MSC-level operating standards that are practical, enforced, and updated as the policy landscape evolves.
Translate MSC AI and automation needs into clear data requirements for the Data Strategy, Reporting & Analytics team. Identify gaps between current data availability and AI readiness, and maintain a living view of AI data dependencies (inputs, features, pipeline requirements, and quality thresholds) to feed into the Analytics team's roadmap. Ensure the digital tooling and platform layer supporting MSC operations is coherent, accessible, and fit for AI-enabled use.
Support the evaluation and business case development for AI and digital initiatives within the MSC. Apply structured frameworks to estimate effort, cost, risk, and expected benefit for each initiative under consideration. Maintain transparency on the portfolio of investments, their projected returns, and their actual outcomes post-implementation. Ensure the MSC is not accumulating AI experiments without accountability for value realization.
Maintain hands-on proficiency with AI platforms for use across the MSC, and design, specify, and govern agentic AI workflows (multi-step automated processes where an AI model takes sequential actions across systems). This includes scoping appropriate use cases, documenting workflow logic, defining human-in-the-loop checkpoints and failure handling, and specifying oversight controls for processes with SOX or compliance exposure. Works with IT and the AI CoE to implement, test, and monitor agentic workflows in production.
Drive systematic adoption of commercial AI tools (including Claude, Microsoft Copilot, and equivalent platforms) across MSC roles, with a focus on measurable productivity outcomes. Identify high-value use cases by role type and service activity, design adoption pathways, and support enablement efforts that move teams from occasional use to embedded practice. Track and report on productivity impact in terms the business can act on, not usage statistics.