Data Scientist

Confirmed live in the last 24 hours

Xebia CEE

Poland

On-site

Posted April 2, 2026

Job Description

Hello, let’s meet!

Who We Are

While Xebia is a global tech company, our journey in CEE started with two Polish companies – PGS Software, known for world-class cloud and software solutions, and GetInData, a pioneer in Big Data. Today, we’re a team of 1,000+ experts delivering top-notch work across cloud, data, and software. And we’re just getting started.

What We Do

We work on projects that matter – and that make a difference. From fintech and e-commerce to aviation, logistics, media, and fashion, we help our clients build scalable platforms, data-driven solutions, and next-gen apps using ML, LLMs, and Generative AI. Our clients include Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, and Allegro or InPost.

We value smart tech, real ownership, and continuous growth. We use modern, open-source stacks, and we’re proud to be trusted partners of Databricks, dbt, Snowflake, Azure, GCP, and AWS. Fun fact: we were the first AWS Premier Partner in Poland!

Beyond Projects

What makes Xebia special? Our community. We run events like the Data&AI Warsaw Summit, organize meetups (Software Talks, Data Tech Talks), and have a culture that actively support your growth via Guilds, Labs, and personal development budgets — for both tech and soft skills. It’s not just a job. It’s a place to grow.

What sets us apart? 

Our mindset. Our vibe. Our people. And while that’s hard to capture in text – come visit us and see for yourself.

About Project

The project focuses on developing intelligent decision-making mechanisms for a large-scale auction and bidding platform. By combining predictive signals, system data, and AI-driven approaches, the system supports and optimizes business decisions across the ecosystem.

You will be:

designing and developing bidding and optimization strategies for CPC-based auction systems, including advanced objectives such as target ROAS,
optimizing auction mechanisms (ranking, pricing, filtering) to balance platform revenue, advertiser value, and marketplace stability,
building and maintaining offline simulation environments, including counterfactual simulations and auction replay systems, to safely evaluate algorithmic changes,
designing and implementing feedback loops and control mechanisms to ensure stable and robust system behavior under dynamic market conditions,
conducting advanced ecosystem-level analytics on large-scale datasets to identify nonlinear effects, equilibrium points, and systemic risks,
designing, executing, and analyzing complex experiments and causal inference studies, accounting for network effects and long-term system impact,
contributing to