AI Senior Product Manager

Senior Product Manager — Analytics Platform NiCE is seeking a Senior Product Manager to join the Analytics Platform team within the Platform LOB. The Analytics Platform is NiCE’s internal AI service layer — the engines and data capabilities that power CXone. It produces AI services including real‑time transcription, post‑call transcription, TopicAI, sentiment analysis, and behavior models that NiCE application teams consume to build customer‑facing products. Success is measured primarily by the breadth of internal service adoption, the quality of services delivered, and the impact those services have on NiCE’s AI differentiation in the market. This is a senior individual contributor role requiring deep product management expertise, demonstrable capability in AI‑native product development, and the ability to operate fluently across two customer relationships simultaneously: the internal NiCE application teams who consume Analytics Platform services, and the enterprise customers who use those applications and expect to engage directly with the capability expert behind them. The Senior Product Manager owns the strategy, roadmap, and prototype‑driven discovery for their area of the Analytics Platform. They move from customer insight to working, validated prototypes — producing detailed specifications that feed directly into the development pipeline. They work alongside the developers who own spec‑driven code execution, with full product management accountability across the service portfolio, internal adoption, and external customer engagement. Responsibilities Service strategy and roadmap Own the roadmap for an Analytics Platform service area — set direction, prioritize, and keep it current Produce and maintain the service specification for each release — a precise, machine‑readable document generated through the prototyping process that functions as the primary prompt for the developers executing the build Own domain expertise for your service area: AI capabilities, competitive landscape, and real‑world production performance Internal adoption and service growth Identify which NiCE application teams should consume each service, understand what is blocking adoption, and drive the changes that unlock it Track and grow the number of NiCE applications actively consuming each Analytics Platform service Work with application PMs to ensure services are positioned, documented, and ready to integrate when they need them Define and communicate the value proposition of each service to internal consumers: accuracy, latency, reliability, and ease of integration Service economics and cost awareness Maintain visibility into the cost dimension of your service portfolio: understand unit economics and monitor cost per service unit delivered Give internal consumers transparent cost and performance data to support informed adoption decisions External customer engagement Engage directly with NiCE enterprise customers as the Analytics Platform subject matter expert — a primary participant wherever Analytics Platform capabilities are in scope Join application PMs in customer meetings — keep the underlying AI capability accurately represented and route customer feedback directly to the roadmap Build direct relationships with enterprise customers, particularly AI governance, risk, and data science functions Turn enterprise customer insight into service requirements: performance gaps, emerging use cases, governance requirements, and integration patterns that only surface through direct engagement Represent the Analytics Platform at customer‑facing sales meetings, events, and user conferences Prototype‑first development Validate ideas and test assumptions with real customers before engineering cycles begin Use prototypes to generate specifications Design and run structured evaluation cycles for AI outputs — establishing baselines, identifying failure clusters, iterating one change at a time, and documenting results AI working practice Apply AI tools across discovery, synthesis, spec writing, competitive analysis, and stakeholder communication Qualifications Experience 8+ years of experience in software product management or a directly relevant role Experience owning a product or service with clear adoption, usage, or commercial metrics — and a demonstrated track record of moving those metrics Demonstrable experience engaging directly with enterprise customers as a technical subject matter expert, including with AI governance, risk, or data science functions Demonstrable experience building AI‑native product prototypes using AI tools, and running structured AI evaluation cycles Experience producing detailed product specifications that enable downstream engineering execution with minimal back‑and‑forth Experience with version control and basic development workflow sufficient to manage a prototype codebase (Git, repo structure, commit discipline) Experience leading and influencing teams that do not report to you in a matrixed global organization Skills Demonstrated ability to grow adoption of a product or service within an organization — understanding what drives and blocks adoption, and how to move it Cost awareness without cost constraint: able to track and communicate service economics clearly, while keeping current AI model costs in proper perspective Engages enterprise customers with technical depth and commercial credibility — equally comfortable discussing accuracy benchmarks, integration patterns, and business value Excellent written and oral communication skills across technical and non‑technical audiences Writes precise, detailed specifications — the foundation on which developers execute spec‑driven development Strong cross‑functional leadership and project‑management skills Hands‑on familiarity with LLM behavior: prompt design, output evaluation, and failure modes Education Bachelor’s degree required. Experience preferred Platform, infrastructure, or API‑layer product management where the direct customer is an internal team or developer Contact center, CX, analytics, or adjacent enterprise software Conversational AI, virtual agents, or generative AI applications — particularly from the perspective of the shared AI services these features consume AI governance considerations — data privacy, audit logging, and regulatory requirements (GDPR, CCPA) — as they apply to AI services consumed across multiple application teams Agentic AI workflows and multi‑step AI orchestration, and how they shape requirements for the underlying AI services NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law. #J-18808-Ljbffr NICE