Machine Learning Product Manager
New
Germany; remote work environment with flexibility to work from anywhere within eligible European locationsFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- Minimum of 5 years of product management experience
- Required Skills
- Artificial IntelligenceMachine LearningProduct ManagementData scienceStakeholder management
Requirements
- Minimum of 5 years of product management experience.
- Several years of experience working with machine learning, artificial intelligence, predictive systems, or algorithmic decision-making products.
- Proven experience collaborating with Data Science and Engineering teams to deliver ML-powered products.
- Strong analytical mindset with expertise in experimentation, hypothesis testing, and performance measurement.
- Ability to evaluate complex business and commercial trade-offs.
- Solid technical fluency with data pipelines, model development lifecycles, and evaluation techniques.
- Experience working in highly strategic, ambiguous, and cross-functional product environments.
- Excellent stakeholder management and relationship-building skills.
- Structured and outcome-oriented approach to product discovery and roadmap development.
- Exceptional communication skills with the ability to simplify and explain complex systems.
- Experience with marketplaces, recommendation systems, ranking algorithms, pricing systems, or ad-tech is highly desirable.
Responsibilities
- Define and own the product vision, strategy, and roadmap for machine learning-powered decisioning and optimization capabilities.
- Translate business objectives into clear product requirements, user stories, decision frameworks, and measurable success metrics.
- Collaborate closely with Data Science, Engineering, Analytics, Commercial, and Leadership teams to develop intelligent systems.
- Identify, evaluate, and prioritize high-impact use cases for machine learning and automated decision-making.
- Lead discovery initiatives to understand existing workflows and operational challenges for AI-driven solutions.
- Partner with technical teams to define data requirements, model objectives, experimentation frameworks, and monitoring strategies.
- Design and implement human-in-the-loop processes that balance automation with transparency, control, and reliability.
- Drive experimentation and validation efforts to assess model performance and mitigate risk.
- Define, monitor, and optimize product KPIs and machine learning performance metrics.
- Communicate complex technical concepts and optimization strategies to both technical and non-technical stakeholders.
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