The Product Manager Advisor - Data & AI Execution & Enablement joins the Data Science and AI department to serve as a dual-hatted leader overseeing both overarching program governance and hands-on project execution. This role involves managing the end-to-end delivery of complex Machine Learning and Generative AI Minimum Viable Products from initial intake through production handoff. Key responsibilities include leading cross-functional engineering pods, identifying reusable technical components like prompt architectures, and managing Voice of the Customer survey programs. You will utilize skills in Agile/Scrum ceremonies, BRD standardization, and executive storytelling to report on ROI and cycle times. Required competencies include experience with Jira, Azure DevOps, and Planview Agility, alongside a deep understanding of LLMs, RAG, and cloud FinOps within a complex enterprise AI delivery pipeline.
Lead the end-to-end execution of complex Data and AI use cases from initial intake through production handoff.
Manage sprint cadences, technical risks, and daily execution for Data Scientists and AI Engineers to ensure on-time MVP delivery.
Facilitate weekly operational syncs to identify cross-project blockers, capture learnings, and report achievements to leadership.
Identify and publish reusable technical components, such as prompt architectures and connectors, to reduce engineering redundancy.
Own the master delivery tracker and standardize execution playbooks, BRD templates, and UAT sign-off procedures.
Manage the Voice of the Customer (VoC) program by surveying participants and translating feedback into process improvements.
Synthesize multi-dimensional metrics into executive reports and create demo videos to showcase AI capabilities to senior leaders.
Operate a structured demand intake process to align business units on project status, prerequisites, and next steps.
What we're looking for
Bachelor’s degree or equivalency in Product Marketing, Economics, Business, or a related field.
Master’s degree in Computer Science, Data Science, Operations Research, Business Analytics, or an MBA with a technical focus (preferred).
5 to work experience in product management, market analysis, research, or an equivalent function.
At least 2–3 years of experience managing Data Science, Machine Learning, or Generative AI project lifecycles.
Direct experience leading complex software engineering, cloud migration, data engineering, or advanced analytics initiatives from concept to deployment.
Proven ability to manage individual high-complexity technical builds and broader program-level governance in a fast-paced enterprise environment.
Mastery of Agile/Scrum ceremonies and proficiency in tools such as Planview Agility, Jira, or Azure DevOps.
Generative AI
Machine Learning
APIs
Data Modeling
Data Governance
Snowflake
Databricks
Microsoft Azure
Amazon Web Services
Google Cloud
Data Pipelines
Knowledge Graphs
Retrieval Architectures
Large Language Models
Data Quality
Observability
Generative AI
Machine Learning
APIs
Data Modeling
Data Governance
Snowflake
Databricks
Microsoft Azure
Amazon Web Services
Google Cloud
Data Pipelines
Knowledge Graphs
Retrieval Architectures
Data Quality
Observability