Machine Learning Product Manager
New
refurbedE-commerce Marketplace
This role is open to candidates based in Europe (including UK) onlyFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 5+ years of product management experience
- Required Skills
- Artificial IntelligenceMachine LearningProduct ManagementData scienceStakeholder management
Requirements
- 5+ years of product management experience, with a few years in ML, AI or algorithmic decisioning
- Strong ability to work with Data Science and Engineering teams on ML-powered products
- Translating business problems into model objectives, product requirements, and measurable outcomes
- Experience with marketplace dynamics, pricing systems, recommendation systems, ad tech, ranking
- Excellent analytical skills and a strong understanding of experimentation
- Ability to reason through complex commercial trade-offs
- Strong technical fluency: comfortable discussing data pipelines, model inputs and outputs, model evaluation, experimentation design, system constraints, and technical trade-offs
- Experience working with ambiguous, cross-functional, and highly strategic product areas
- Strong stakeholder management skills
- Structured, outcome-oriented approach to product discovery and delivery
- Excellent communication skills
Responsibilities
- Own the product vision, strategy, and roadmap for refurbed’s AI-powered steering capabilities
- Translate the company and product vision into clear product problems, business requirements, decision domains, user stories, and measurable success criteria
- Partner closely with Data Science, Engineering, Analytics, Commercial teams and Leadership teams to define how ML models and decisioning systems should support marketplace growth
- Identify and prioritize the most valuable use cases for intelligent steering
- Work with Data Science and Engineering to define the data inputs, model requirements, experimentation approach, monitoring needs, and feedback loops required to make decisioning reliable and scalable
- Lead discovery into current commercial workflows, pain points, and decision logic across various systems
- Design product mechanisms for human-in-the-loop control
- Use experimentation to validate recommendations and de-risk automation before scaling
- Define and track product KPIs and model-impact metrics, ensuring that ML-driven decisions translate into real business outcomes
- Communicate complex ML, data, and optimization concepts clearly to both technical and non-technical stakeholders
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