ConvergeSPORTS - Technical Product Manager - Analytics & Insights - Innovation_Delivery_Transformati

The Team

ConvergeSPORTS is a product-driven business combining Deloitte's sports industry experience with proprietary data, AI-native decision intelligence, and reusable Converge capabilities.

We help sports organizations grow revenue, deepen fan engagement, optimize partnerships, and improve commercial performance while operating with the focus of a product company.

The Analytics & Insights product suite turns sports, fan, sponsorship, partnership, audience, and commercial data into reusable decision capabilities.

Its products combine governed data, analytical models, and intuitive workflows to help sports organizations identify opportunities, prioritize actions, and measure commercial outcomes.

The Product Management team defines how these technical capabilities become scalable products.

It works across Data Science, Data Engineering, Software Engineering, Design, and Go-to-Market to translate market needs into clear product requirements, technical priorities, and repeatable product experiences.

Position Summary

ConvergeSPORTS is seeking a Technical Product Manager for Analytics & Insights to own the translation between market needs, product strategy, data architecture, analytical capabilities, and engineering execution.

You will define technically grounded roadmaps, convert complex data and model requirements into buildable product increments, and ensure new capabilities are reliable, reusable, and commercially relevant.

This role is ideal for a product leader who can operate comfortably with engineers and data scientists while maintaining a clear view of user value and product outcomes.

Recruiting for this role ends on 10/31/2026.

Work you'll do

You will manage the technical product lifecycle for Analytics & Insights, from discovery and architecture alignment through backlog execution, launch, adoption, and continuous improvement.

Your work will include:


* Define and maintain the product vision, roadmap, success measures, and release priorities for Analytics & Insights capabilities.


* Translate user, buyer, and business needs into technical product requirements, user stories, acceptance criteria, data contracts, API requirements, and workflow specifications.


* Partner with Data Science to define model use cases, output requirements, explainability needs, evaluation criteria, and product integration patterns.


* Partner with Data Engineering and Software Engineering to align roadmap priorities with architecture, platform dependencies, scalability, security, observability, and technical debt.


* Own backlog prioritization and sprint readiness, including story refinement, dependency management, sequencing, and acceptance of completed work.


* Define how data, analytical outputs, recommendations, and AI-assisted features appear within product workflows and support user decision-making.


* Establish measurable launch and adoption criteria, including product usage, reliability, data quality, time to insight...