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CelonisCelonisNew York, NY

Product Manager

Product Manager owning the Decision Intelligence layer (real-time decisioning and predictive capabilities) within Celonis Context Model. Partners with PhD AI/ML researchers and engineers to scope, prioritize, and deliver foundational model features from research to enterprise commercialization. Requires 5+ years PM experience in technical AI/ML domains.

204k – 240k
Hybrid5+ YOEProduct Management

About the role

Responsibilities

  • Define scope and sequence: Partner with Engineering to turn foundation model capabilities (aiCast, aiMatch) into well-scoped, shippable enterprise product phases.
  • Own the delivery timeline: Lead execution alignment, ruthlessly prioritizing the product backlog to ensure delivery of the right features with the right technical scope at the right time.
  • Bridge research and commercialization: Act as strategic connective tissue between engineering milestones and customer needs, translating complex technical limitations or breakthroughs into predictable roadmap timelines.
  • Optimize the model lifecycle: Collaborate with ML platform engineers to optimize model onboarding, evaluation pipelines, and user-facing feedback loops for frictionless deployment.
  • Facilitate cross-functional execution: Coordinate with Frontend, Design, and Applied AI teams to ensure seamless integration into the core platform and customer solutions.

Requirements

  • 5+ years of Product Management experience in highly technical domains such as AI platforms, developer tools, MLOps, or complex enterprise data infrastructure.
  • Engineering-first mindset: Respect and understand deep technical architecture; comfortable challenging and being challenged by engineers on technical scope, trade-offs, and dependencies.
  • Technical data fluency: Strong comfort with structured/time-series data, model evaluation metrics (e.g., precision, recall, error rates), and data pipeline architectures.
  • Systems thinking and scoping mastery: Proven ability to break down highly complex, ambiguous technical projects into logical, incremental releases without slowing engineering momentum.
  • Exceptional communication: Ability to explain complex ML infrastructure concepts to business stakeholders and translate business requirements into structured technical scope for engineers; comfortable working as connective tissue across teams in a fast-paced environment.

Nice-to-Haves

  • Experience partnering with PhD-level AI/ML researchers.
  • Background in real-time decisioning, predictive modeling, or foundational model capabilities.

Skills

Product ManagementAi PlatformsMLOpsMachine LearningData PipelinesModel EvaluationTechnical ArchitectureSystems ThinkingAicastAimatch
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