Analytics Product Owner

Dubois Chemicals•Sharonville, OH

About The Position

Accountable for the end-to-end success of an analytics software platform that integrates customer data to deliver trended insights and AI-driven recommendations. This role ensures the product delivers measurable business value, including improved customer retention, increased chemical sales, and profitable equipment growth. This role will enable a digital transformation into a data-driven, insight-led service model, improving customer outcomes while unlocking new growth through analytics, digital differentiation, and smarter chemical program management.

Requirements

10 years of project/product management experience
Proven track record of leading high-performing teams and delivering complex projects
Sales and customer service experience is an asset
Ownership mindset: accountable for outcomes, not just delivery
Customer-centric: grounded in field and customer reality
Data-driven: uses metrics to prioritize and validate decisions
Commercially focused: ties product work to revenue and profitability
Cross-functional leader: aligns engineering, field, and commercial teams
Bachelor’s degree in engineering (Chemical, Mechanical, Environmental, or Industrial)
MBA or extensive business management experience

Responsibilities

Own product vision, roadmap, and backlog aligned to business priorities
Deliver a scalable platform progressing from: Data capture → trended insights → predictive & prescriptive analytics
Ensure high-quality, on-time delivery through Agile execution
Lead pilot programs and field testing to validate usability, data quality, and value
Drive adoption by aligning product capabilities with real-world workflows
Scale from pilot to full deployment across target customers
Partner with commercial teams to define value proposition and enable successful launch
Support sales, field teams, and customer onboarding
Remove adoption barriers and drive sustained usage
Develop and maintain the product business case
Establish and manage the project budget including costs for development and implementation
Continuously refine assumptions using actual performance data
Define, track, and communicate key performance metrics: Customer retention / churn reduction, Incremental chemical sales, Profitable equipment sales, Product adoption and engagement
Ensure clear linkage between product capabilities and financial outcomes
Identify and mitigate: Technical risks (data integration, scalability, AI readiness), Operational risks (data quality, usability, field adoption), Commercial risks (pricing, value realization)
Drive roadmap toward machine learning–based insights and recommendations
Partner with IT to define use-cases, data requirements, and success metrics