Product Manager MLops
Job description
About the role
You will define and own the end-to-end product strategy for Scaleway's MLOps portfolio, translating complex user workflows into a clear and compelling product vision. You will lead the discovery, validation, and delivery of a managed MLflow offering that meets the needs of data scientists and ML engineers across Europe. In this role, you act as the primary "Voice of the Customer," bridging the gap between technical teams and market demands to ensure the product delivers real business value. You will partner closely with engineering to design iterative releases that are robust, scalable, and aligned with sovereign cloud principles. Your work will directly influence how Scaleway positions itself as a key European infrastructure provider for machine learning workflows. You will balance strategic roadmap thinking with hands-on execution to ensure fast, measurable product delivery.
Key facts
What you'll do
Define and articulate a clear product vision and multi-year roadmap for Scaleway's MLOps offering, ensuring alignment with the broader Scaleway cloud strategy.
Launch a managed MLflow service on Scaleway, owning end-to-end delivery from concept to production while coordinating closely with engineering squads.
Conduct discovery interviews with data scientists and ML engineers to validate customer problems and translate insights into prioritized product requirements.
Collaborate daily with cross-functional teams, including engineers, designers, and data specialists, to ensure timely delivery of high-quality product increments.
Design and monitor key product metrics and adoption indicators to evaluate success and drive data-informed decisions.
Ensure the MLOps platform integrates seamlessly with existing Scaleway services, creating a cohesive and developer-friendly ecosystem.
Break down complex user workflows into actionable features and experiments, balancing speed of delivery with long-term architectural integrity.
Champion best practices in product management, from hypothesis definition to experimentation, to continuously improve the offering.
Work closely with customer-facing teams to gather feedback, refine positioning, and identify new opportunities for differentiation in the European cloud market.
Represent the product internally and externally, building trust and alignment with stakeholders to secure buy-in for the vision and roadmap.
Translate technical constraints and opportunities into clear guidance for engineering, ensuring the team can deliver reliable and performant solutions.
Drive the prioritization process, making trade-off decisions that maximize customer impact and business value within defined constraints.
Contribute to the development of AI and cloud infrastructure strategy by incorporating market trends, competitive analysis, and customer feedback into product planning.
Foster a culture of collaboration and learning within the team, encouraging knowledge sharing and continuous improvement across product and engineering functions.
Requirements
Bring 2 to 5 years of experience working on technical products or platforms, with a strong track record of delivering measurable outcomes.
Demonstrate prior experience working on Machine Learning, Data Platform, or MLOps-related products, with a clear understanding of the associated challenges.
Show deep familiarity with Machine Learning production environments and workflows, including how models are trained, deployed, and monitored in real-world scenarios.
Prove you can collaborate effectively with highly technical teams, including engineers, data scientists, and ML engineers, speaking their language without needing to write code.
Exhibit hands-on familiarity with technologies such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or Jupyter Notebook, showing how you have used them to solve problems.
Have a background as a Data Engineer or Data Scientist transitioning into Product Management, which provides credibility in assessing technical feasibility and trade-offs.
Understand production ML systems, deep learning concepts, neural networks, and the MLOps ecosystem well enough to engage in detailed discussions with technical stakeholders.
Possess strong prioritization skills, enabling you to focus on high-impact initiatives in fast-moving, ambiguous environments while maintaining clarity on goals.
Nice to have
Experience working in European tech environments with an understanding of regional regulatory and compliance considerations.
Background in open source contributions or engagement with open source ML communities.
Familiarity with cloud provider ecosystems, billing models, and multi-tenant architectures relevant to managed services.
Experience with DevOps practices and infrastructure-as-code concepts relevant to ML workflows.
Practical notes
This is a full-time, long-term position based primarily from our Paris office.
Hybrid work policy allows up to 3 days of remote work per week, supported by our modern collaborative offices across multiple French cities.
The role reports to Fredéric Bardolle and will sit within a newly formed team that will grow over time alongside the MLOps product line.
No specific compensation details are disclosed in this opportunity description.
Candidates must be able to start promptly and commit to the full delivery cycle of the initial managed MLflow offering.
Travel is limited to within France and European regions for customer visits or industry events, as required by the role.
This position is open to candidates who meet the hard requirements, with willingness to learn and grow into the broader Scaleway product context.