Data Scientist – Ad Intelligence

As a Data Scientist on our Ad Intelligence team, you’ll build the models and algorithms that estimate how advertisers spend money and reach audiences across the web. This role demands a genuine understanding of the business logic behind our numbers, combined with the technical range to solve problems at any scale: sometimes the right answer is a carefully tuned heuristic shipped in a day, sometimes it’s a full ML pipeline that takes weeks to get right. Whichever approach is the right one, you will own the algorithm end-to-end.

Responsibilities

Build a deep understanding of the business logic behind our metrics, and encode it into model assumptions, features, and validation rules; Design, build, and sometimes productionize models that estimate ad intelligence metrics; Apply modern ML techniques where they earn their complexity; use simpler heuristics where they don't; Own tasks end-to-end from exploration and proof of concept through a full research cycle including careful model selection and evaluation; In some cases create the production implementation in collaboration with engineers; Collaborate with data analysts to uncover and solve data quality issues; Document and communicate the assumptions and limitations behind each model; Use modern AI-assisted tooling (Claude, Cursor, and similar) as a core part of daily workflow to move faster

Requirements

2-4 years of experience building and deploying machine learning models or statistical algorithms in production; Strong Python skills, and hands-on experience with modern ML algorithms; PySpark experience, or the ability and willingness to learn it quickly; Comfort using AI coding tools (Claude, Cursor, or similar) as part of everyday workflow; A genuine drive to understand the business logic behind the numbers, not just optimize a metric in isolation; Pragmatism: judgment to know when a simple heuristic beats a complex model, and discipline to build the complex one when needed; Analytical Thinking: using analytical and statistical rigor to solve ambiguous problems and translate them into business value; Strategic Thinking: weighing complexity against time available, and picking the modeling approach that best serves the timeline and business need; Ownership: taking a problem from raw data to a validated, production model without needing to be chased; Learning Agility: comfortable picking up new tools, data sources, and techniques quickly, including AI-assisted development workflows; Collaboration: working effectively with others within and across teams

Nice to have

MSc in Computer Science, Statistics, Mathematics, or a related quantitative field (preferred); Experience with big data in a cloud environment (Databricks)

Benefits

5 weeks of vacation; Extra day off during your birthday month; 3 sick days or a multisport card; MacBook Pro M3, dual monitors, and electric standing desk; Team-building events, parties, weekly lunches, and happy hours; Access to Equity programs; Career Week, personalized coaching, and ongoing learning solutions; Modern office in DOCK IN area (Prague 8) stocked with snacks, drinks, fruits