System.AI is a Schneider Electric global CTO/ETO Center of Excellence delivering predictive insights and Condition-Based Maintenance (CBM) outcomes for complex power and cooling systems. Today, System.AI primarily supports Data Center environments, with planned expansion into additional market segments requiring advanced analytics and industrialized CBM capabilities (industrial facilities, healthcare, energy distribution, microgrids and other mission-critical infrastructures).

In partnership with AVEVA and technology partners, System.AI designs, deploys, and evolves analytics across cloud, edge, and industrial environments to increase asset reliability, service efficiency and operational excellence.

As Analytics Technical Portfolio Manager, you own the end-to-end analytics lifecycle for the System.AI portfolio and ensure strong alignment with Service LOB (EcoCare DC, Cooling, and future segment offers).

For this U.S. based position, the expected compensation range is $162,000 - $185,000 per year, which includes base pay and short-term incentive.

The compensation range for this full-time position applies to candidates located within the United States. Our salary ranges are determined by reviewing roles of similar responsibility and level. Within the salary range, individual pay is determined by several factors including performance, knowledge, job related skills, experience, and relevant education or training. Schneider Electric also offers a comprehensive benefits package to support our employees, inclusive of medical (with member reward points), dental, vision, and basic life insurance, Benefit Bucks (credits to apply towards your benefits) flexible work arrangements, paid family leaves, 401(k) + match, well-being and recognition (including service anniversary) programs, 12 holidays per year, 15 days of paid time off per year (pro-rated in the first year of employment based on start date), opportunity to purchase company stock (eligibility depends on start date), and military leave benefits.

You must submit an online application to be considered for the position. The Company will accept applications on an ongoing basis until the position is filled.

Key Responsibilities

End to End CBM Outcome & Analytics Lifecycle Ownership

Own the full Analytics Lifecycle across all System.AI segments:

Ideation → Incubation → Validation → Transfer to Industrialization → Bespoke Lifecycle Management

Maintain long-term ownership of customer-specific analytics not intended for standardization

Translate Service LOB requirements into validated, measurable CBM outcomes that reduce interventions, improve uptime and mitigate risk

Define performance criteria and monitor analytics performance across deployments and customer segments

Support next-generation analytics deployment (agentic models, LLM-enabled CBM workflows, edge intelligence)

Coordinate with People.AI and platform teams to prepare next-generation deployment frameworks

Solution Tooling & Process Ownership

Design, manage and maintain System.AI tooling used to track, validate and govern analytics assets

Ensure lifecycle traceability, versioning and controlled evolution across cloud, edge and OT environments

Support SME’s and the Analytics Impact Leader in outcome definition, documentation and change-management processes

Client Engagement & Delivery

Engage with customers across segments to define, validate and refine CBM analytic outcomes

Lead tuning and validation cycles during pilot phases and deployment stages

Capture segment specific operational insights and feed them back into System.AI and LOB roadmaps

Operational Insights

Support recruitment and coordination of internal and contract technical resources

Strengthen integration with Schneider Electric platforms (EBO, PME, PSO, DCIM, EAA and future segment-specific platforms)

Contribute to the evolution of Schneider Electric’s edge, IoT and analytics standards for cross-segment scalability

Required Skills & Experience

Information Technology:

Strong experience with Windows and cloud environments (Azure preferred)

Knowledge of Cloud DevOps practices, cloud optimization and cost-optimization strategies

Understanding of cybersecurity practices including network traffic whitelisting and secure configuration

Operational Technology:

Experience with industrial communication protocols such as OPC and Modbus

Understanding of OT networks and OT cybersecurity principles

Industrial & Analytics Expertise:

Experience creating and managing industrial asset models

Hands-on experience with predictive analytics, industrial data engineering and analytic toolsets

Familiarity with industrial and data center environments preferred

Experience in industrial services or field-service operations is an advantage

Technical Tools & Platforms:

Python, C#, Postman

Azure services (Azure ML experience is an advantage)

API integration and cloud-based data interchange

Analytics and monitoring tools related to UPS, cooling, switchgear, batteries and other critical systems

Competencies:

Strong analytical thinking and problem solving skills

Ability to work across IT/OT boundaries

Excellent communication skills with customers and internal teams

Ability to lead deployments and manage multiple global initiatives

Comfortable with travel and client-facing interactions

Motivates and aligns SME’s, Service LOB, engineering partners and external stakeholders

Builds a collaborative, inclusive working environment across global teams and segments

Encourages open communication, shared ownership and cross-functional problem solving

Recognizes contributions, fosters trust and drives collective engagement toward shared outcomes