Product Manager

TEKsystems•Charlotte, NC

1d•Hybrid

About The Position

This Product Manager role focuses on operational data and AI capabilities. The individual will be responsible for product management related to data lakes and operational data, including asset management aspects like server inventory, software on servers, and patching status. The core function is to define and deliver the vision, roadmap, and execution for operational data platforms and AI capabilities, enabling real-time insights, automation, and predictive analytics across the enterprise. This role will define and execute the vision, strategy, and roadmap enabling real-time insights, automation, and predictive analytics across the enterprise.

Requirements

Product management
Data
AI
7+ years in product management, with focus on data platforms, analytics, or AI.
Proven track record of delivering data-driven products in large-scale enterprise environments.
Familiarity with data platforms (e.g., Snowflake, Azure Synapse), streaming technologies (Kafka), cloud services (AWS, Azure, GCP), and ServiceNow.
Understanding of AI/ML lifecycle, model deployment, and MLOps.
Knowledge of data governance, security, and compliance frameworks.
Strong stakeholder management and communication skills.
Ability to translate technical capabilities into business outcomes.
Data-driven decision-making and strategic thinking.

Nice To Haves

Experience with operational data domains (IT operations, infrastructure telemetry, business process data).
Certifications in cloud data services, AI/ML, or product management (e.g., SAFe POPM).

Responsibilities

Develop and communicate the product vision for operational data and AI enablement aligned with enterprise goals.
Partner with engineering and data teams to deliver scalable, secure, and governed operational data platform.
Ensure integration with existing systems, APIs, and cloud-native architectures.
Define the end-to-end integration plan between the operational data platform and ServiceNow (Incidents, Changes, CMDB, Requests, Discovery).
Prioritize use cases: auto-ticketing, ticket enrichment, change risk scoring, proactive problem detection, CMDB health, and AI-assisted triage.
Identify and prioritize AI-driven use cases (predictive maintenance, anomaly detection, process optimization).
Collaborate with data science teams to operationalize models and embed AI into workflows.
Drive adoption through enablement, training, and clear communication of value.
Act as a liaison between business units, engineering, and data science teams.
Define KPIs such as data availability, AI model accuracy, operational efficiency gains, and adoption rates.
Use telemetry and feedback to continuously improve the product.