Defense Engines AI Transformation Leader

Job Description Summary
Transform engine and spare-part delivery for Defense Engines & Services (DES). This role owns the product vision, roadmap, and end-to-end lifecycle for the data, analytics, and artificial intelligence (AI) capability that makes that transformation possible. Act as the customer representative for a portfolio of interdependent products spanning new engine production and Maintenance, Repair & Overhaul (MRO), from demand and forecast through fulfillment and service and support. Success is measured through DES KPIs, led by on-time delivery and spare-part output, then inventory and net productivity.

This is a forward-deployed role. You will work alongside the planners, operators, and engineers who own the delivery, and you will build digital capability with them, taking AI-enabled ideas from concept to medium and high-fidelity working prototypes on real production data.

Job Description

This role will also require in-person attendance for New Hire Orientation on Day 1

Roles and Responsibilities

In this role, you will:

- Own all aspects of the lifecycle (planning, forecasting, production) for multiple interdependent data, analytics, and AI products that together form the DES delivery transformation program and represent the customer in every scope and sequencing tradeoff.

- Build and maintain the DES data and analytics product roadmap, sequence it against the constraints that actually limit engine and spare-part output, and identify and secure the stakeholders needed to support it.

- Stand up self-service data and analytics on manufacturing, quality, and delivery data so planners, buyers, and shop leaders answer their own questions in the moment rather than opening a DT ticket. This closes the single largest capability gap in the portfolio.

- Operationalize and sustain value from advanced analytics and agentic AI investments, and move proven prototypes inside the operating model rather than leaving them adjacent to it, where they stay advisory.

- Partner with central DT and Enterprise Data on data fabric and ontology adoption so DES manufacturing, quality, and delivery data is modeled once and reused across products.

- Take AI-enabled ideas from concept to working software. Build low-fidelity proofs to test feasibility, then advance the survivors to medium and high-fidelity prototypes on real production data, in the hands of real users, and decide quickly which ones earn production investment and which get killed.

- Work forward-deployed. Embed with operators, planners, and engineers at the Genba, build with them rather than collecting requirements and handing them off, and stay on the problem until the capability is in use.

- Use rapid prototyping as a countermeasure inside Daily Management, converting Key Performance Indicator (KPI) and Targets to Improve (TTI) gaps into working capability in weeks rather than quarters.

- Define the value case in delivery terms up front (engine days, turnaround time, schedule adherence, inventory turns, cost), drive early materialization of benefits, and report outcomes against the 2026 DES measures. Stop or redirect work that does not move a delivery number.

- Influence Executive Band (EB) leaders and below on investment decisions, and partner with business, finance, and DT leadership to align Digital Technology spend with value stream priorities.

- Set data and AI standards across the DES horizontal so use, implementation, support, and technical integrity stay consistent and do not fragment by product line.

- Operate as a player-coach. Mentor product managers and engineers on systems thinking for large-scale problems, lean and experimental product practice, and raise the team's data and AI fluency.

- Manage supplier and contractor partners delivering analytics and AI work, hold them to outcomes, and reduce the team's dependence on expensive external delivery.

- Lead risk management for the portfolio, including export control, cybersecurity, data governance, and responsible AI, and design controls into products instead of relying on exception processes.

Business Acumen

- Translates business problems into product bets, quantifies the value, and makes tradeoffs across a multi-product portfolio based on business priority rather than the loudest request.

- Establishes performance metrics and drives continuous improvement across systems, processes, and resources.

- Demonstrated customer focus; evaluates decisions through the eyes of the operator on the floor and partners with the business to shape future initiatives.

- Decomposes problems and decides when the problem or the solution is not fully defined.

- Constructs multiyear scenarios for data-centric methods and technologies under uncertainty, and interprets industry trends that should inform the roadmap.

Leadership

- Sets and changes direction at the enterprise level by building direct and indirect support for ideas, largely without formal authority.

- Leads and influences across a complex matrix organization, including Executive Band leaders and their teams, and incorporates stakeholder input while still deciding.

- Directs and mentors others toward a systems thinking approach for solving large-scale problems, and removes roadblocks on behalf of the team.

- Owns decisions, including outcomes and any required rework plans.

- Acts as a catalyst for change, rewards early adopters, and builds a culture of proactive problem solving rather than reactive break-fix.

Personal Attributes

- Excellent written and oral communication; presents concise facts and the implications of alternatives in a way that is relevant to senior leadership and customers.
- Strategic thinker who converts vision into a concrete action plan with named owners and dates.

- Manages uncertainty for self and others, and makes confident decisions when problems or solutions are not 100% defined.

- Curious and creative; removes barriers to the creativity of others and pushes simplicity and speed.

- Biased toward demonstration over documentation. Would rather show a working prototype than present a plan for one.

- Strong analytical, project management, and organizational skills, with the ability to prioritize under pressure and tight deadlines.

Required Qualification

Bachelor’s degree from accredited university or college with minimum of 10 years of professional experience OR Associates degree with minimum of 13 years of professional experience OR High School Diploma with minimum of 15 years of professional experience

Minimum 8 years of professional experience in IT

Note: Military experience is equivalent to professional experience

Ability to travel; 10-50%

Eligibility Requirement:

-Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job.

Desired Qualification

Technical Expertise

- Hands-on builder with a forward-deployed engineering mindset. Personally produces working artifacts (data pipelines, ontology and semantic models, notebooks, agent workflows, and functional user interfaces) rather than only specifying them, and is fluent in Python, SQL, or equivalent.

- Demonstrated ability to move an idea across fidelity levels under time pressure: proof of concept to prove feasibility, medium fidelity to prove the workflow, high fidelity to prove production readiness and earn investment.

- Demonstrated ownership of data platform and advanced analytics products at scale, covering ingestion, modeling, governance, performance, and self-service consumption.