# Product Manager

**Company:** [Celonis](https://hotfix.jobs/companies/celonis)
**Location:** New York, NY
**Role:** Product Management
**Salary:** $204k – $240k/yr
**Experience:** 5+ years
**Skills:** Product Management, Ai Platforms, MLOps, Machine Learning, Data Pipelines, Model Evaluation, Technical Architecture, Systems Thinking, Aicast, Aimatch
**Posted:** 2026-07-07

> 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.

## Job Description

## 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.

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