Own the end-to-end product lifecycle for ML-powered scoring within Sponsored Offers. Define product vision, strategy, and measurable outcomes; lead discovery and prioritize opportunities; coordinate Product, Data Science, Engineering, and business stakeholders; optimize roadmaps, define success metrics, and use AI-assisted techniques to accelerate decisions and improve outcomes.
Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
You will be:
- owning the end-to-end product lifecycle for ML-powered scoring capabilities within the Sponsored Offers domain,
- defining product vision, strategy, and measurable outcomes aligned with business objectives,
- leading product discovery by identifying customer and business problems, validating hypotheses, and prioritizing opportunities,
- orchestrating cross-functional collaboration between Product, Data Science, Engineering, and business stakeholders throughout the delivery process,
- optimizing the product roadmap by balancing business value, user needs, technical feasibility, and strategic priorities,
- defining success metrics and monitoring product performance to continuously improve business outcomes,
- communicating product direction, priorities, and progress to senior stakeholders across the organization,
- leveraging AI-assisted discovery and research techniques to accelerate product decisions and improve product quality.
- proven experience as a Product Manager building and scaling digital products,
- experience defining product strategy and delivering medium- to long-term product objectives (6–12 months),
- strong ability to identify business problems, define measurable product outcomes, and focus on value rather than delivery alone,
- practical experience working on ML-powered products and collaborating closely with Data Science and Engineering teams,
- strong understanding of product discovery, prioritization, and outcome-driven product management,
- experience making product decisions based on business KPIs within e-commerce, marketplace, advertising, or similar digital environments,
- excellent stakeholder management skills with experience collaborating with senior leadership and cross-functional teams,
- experience using AI-powered tools to support product discovery, research, and decision-making,
- fluent English communication skills,
- practical experience using AI-powered assistants (e.g. ChatGPT, Claude, Copilot Chat, or similar) to improve productivity, quality, or decision-making in analysis and software delivery.
Work from the European Union region and a work permit are required.
Nice to have:- experience in AdTech or ranking and recommendation products,
- experience working with experimentation platforms and A/B testing,
- experience collaborating with platform or data teams on ML and data capabilities,
- understanding of auction-based monetization models and their product trade-offs,
- experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily analytical and documentation work,
- interest in and familiarity with emerging AI-driven practices (e.g. automation of analysis tasks, AI-supported documentation, or workflow optimization), with a willingness to explore and experiment beyond standard approaches.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
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