Product Manager

The Role
As a Product Manager for Decision Intelligence, you are the anchor for delivering high-quality products to our customers at enterprise scale. You will partner intensely with PhD-level AI/ML researchers and core platform engineers who drive our foundational model capabilities.

Specifically, you will own Celonis/Ikigai intelligence, a first-class citizen in the Celonis Context Model. Partnering deeply with ML engineering, AI platform, and infrastructure teams, you will lead the delivery of the model’s real-time decisioning and predictive layer.

What You’ll Do (Responsibilities)
Define Scope & Sequence: Partner closely 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 we deliver the right features, with the right technical scope, at the exact right time for our customers.
Bridge Research and Commercialization: Act as the 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, ensuring a frictionless deployment experience.
Facilitate Cross-Functional Execution: Coordinate with Frontend, Design, and our Applied AI teams to ensure our capabilities are seamlessly integrated into the core platform and then into customer solutions.

What We’re Looking For (Requirements)

5+ Years of Product Management Experience: Specifically within highly technical domains such as AI platforms, developer tools, MLOps, or complex enterprise data infrastructure.
An Engineering-First Mindset: You respect and understand deep technical architecture. You are comfortable challenging and being challenged by engineers on technical scope, trade-offs, and dependencies.
Technical Data Fluency: Strong comfort with the mechanics of structured/time-series data, model evaluation metrics (e.g., precision, recall, error rates), and data pipeline architectures.
Systems Thinking & Scoping Mastery: Proven ability to break down highly complex, ambiguous technical projects into logical, incremental releases without slowing down engineering momentum.
Exceptional Communication: The ability to explain complex ML infrastructure concepts clearly to business stakeholders, and conversely, translate messy business requirements into structured technical scope for engineers. You work across teams and are comfortable being the connective tissue between teams in a fast-paced environment.