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 measurement of recommendation experiments by designing A/B tests, defining success metrics, and delivering readouts that explain not just whether something worked but why,
- investigating model and system behavior, including ranking quality, retrieval coverage, exposure and feedback effects, and the gaps between offline metrics and online outcomes,
- building the dashboards, data models, and self-service tooling the team relies on to monitor recommendation health, while leaving them in a state the team can maintain after the engagement,
- collaborating with engineers and PMs to scope analytical questions clearly by clarifying objectives and sizing opportunities before they turn into long projects,
- proactively surfacing insights, such as unusually behaving segments, drifting metrics, and counterfactuals worth testing.
Your profile:
- working from the office in Stockholm (mandatory),
- 5+ years in an analytics or data science role, with a track record of operating independently,
- direct experience with recommendation, search, ranking, ads, or another large-scale ML-driven product,
- strong SQL and comfort with large datasets (we work in BigQuery),
- fluency in Python for analysis,
- a real grasp of experimentation - A/B testing, statistical significance, and the common traps (peeking, interference, novelty effects),
- an instinct to clarify the objective before reaching for a solution, and the discipline to separate what the data shows from what you'd like it to show,
- clear communication: you can take a messy analysis and land a single, defensible takeaway for any audience, from an engineer to a director,
- a very good understanding and application of advanced statistics.
Nice to have:
- familiarity with recommender systems internals - retrieval, ranking, candidate generation, offline vs. online evaluation,
- exposure to bandits, off-policy evaluation, or causal inference,
- experience building data models and dashboards others depend on (dbt, Looker, or similar).
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision