Sr. Product Manager - Conversational Analytics

3M•Maplewood, MN

7d•$221,591 - $270,834•Onsite

About The Position

As the Senior Product Manager, Conversational Analytics, you will lead the strategy and execution of next-generation data, AI, and machine learning capabilities across 3M. This role is responsible for building and scaling intelligent data products, knowledge graphs, and conversational analytics solutions that transform traditional reporting into AI-driven decision intelligence. Partnering across business and technology teams, you will drive the design and delivery of modern data and semantic solutions that enable business innovation, operational excellence, and smarter enterprise decision-making at a global scale. This role offers the opportunity to shape how data, AI, and decision intelligence transform a global enterprise. You will lead the design and delivery of modern solutions that combine data engineering, AI/ML, and semantic data foundations to power business innovation, operational excellence, and enterprise decision-making. Leading the strategic direction and execution of Intelligent data products initiatives across the 3M. This role will focus on harnessing the power of data and AI/ML, to drive business innovation and operational excellence across the company. This role requires a strategic thinker with a deep understanding of technology and its application in business contexts.

Requirements

Bachelor's degree in computer science, Data Science (completed and verified prior to start)
Ten (10) years of experience leading data, analytics, AI/ML initiatives in a private, public, government, or military environment
Five (5) years of deep expertise in designing, architecting and delivering data engineering, ML platforms, and generative AI ecosystems

Nice To Haves

Data Lakehouse architectures, Graph Data Architectures and cloud‑native engineering.
Intelligent data products or data-as-a-product programs including Knowledge graphs and semantic data layers
Generative AI solutions, with preferred experience building conversational data analysis solutions
AI driven 360 degree solutions such as Customer 360, Supplier
Predictive AI/ML Models in Marketing, Supply Chain and Finance domains (Customer Behavior Predictions, Demand Planning, Financial Forecasting, Customer Lifetime Value, Customer Churn etc)
AI Ops and ML Ops managing the full lifecycle of AI/ML Models including Data Drift, Model Drift and Model Risk Management
Enterprise data catalog and metadata platforms

Responsibilities

Lead the transition from descriptive analytics to Decision Science, building intelligent products that provide proactive recommendations across the enterprise.
Drive the development of Conversational Data Analysis solutions that allow users to query data via natural language, moving away from rigid dashboarding to fluid, role-based insights.
Drive the roadmap of predictive models, tools and utilities that will be leveraged by AI Agents to drive automated decisions in business processes across Commercial, Operational and Enterprise functions.
Implement decision science frameworks to support strategic, operational, and financial decision-making, enabling predictive and prescriptive insights that drive measurable business outcomes.
Develop insight systems that deliver real-time, context-aware intelligence tailored to specific roles—from executives to frontline operators.
Establish robust enterprise capabilities in Data Cataloging, Metadata Management, and Semantic Data Fabric architecture to ensure high-fidelity data is ready for AI/ML consumption.
Build and scale enterprise Knowledge Graphs and Business Ontologies to unify structured and unstructured data, enabling deep reasoning and power context aware enterprise search.
Build context-aware enterprise data layers, ensuring data is ready for consumption by advanced AI models and automation tools across R&D, Manufacturing, Supply Chain, and Commercial functions.
Ensure alignment on architectural standards, unified data governance, and operational reliability through specific collaboration with the Data Platform, Data Engineering, AI Engineering, Product Management, Master Data Management (MDM), and Data Observability teams.
Drive the adoption of intelligent insights across the value chain—including Product Development, Portfolio Management, Operations, Sales, and Customer Experience—serving as a strategic advisor to senior leadership.
Establish governance models for data quality, model reliability, trust scores, semantic consistency, and Responsible AI.
Comply with corporate policies, procedures and security standards while performing assigned duties.
Contribute to a strong Environmental Health and Safety (EHS) culture by following safety policies, identifying hazards, and engaging in continuous improvement.