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.

• 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; and 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 you will also create the production implementation in collaboration with engineers.

• Closely collaborate with data analysts to uncover and solve data quality issues.

• Document and communicate the assumptions and limitations behind each model, so stakeholders know exactly what the numbers do and don't capture.

• Use modern AI-assisted tooling (Claude, Cursor, and similar) as a core part of your daily workflow to move faster.

The mindset that will help you succeed

• 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 the business need with respect to future sustainability and maintainability.

• 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.

• MSc in Computer Science, Statistics, Mathematics, or a related quantitative field (preferred); strong practical experience can substitute a degree with knowledge in relevant fields (statistics, machine learning, advanced mathematics).

• 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 (with our support!). Our existing solutions run on Spark, and you’ll need to read, use, and build on that code effectively.

• Experience with big data in a cloud environment is an advantage; we use Databricks daily.

• Comfort using AI coding tools (Claude, Cursor, or similar) as part of your everyday workflow.

• A genuine drive to understand the business logic behind the numbers, not just optimize a metric in isolation.

• Pragmatism: the judgment to know when a simple heuristic beats a complex model, and the discipline to build the complex one when it’s actually needed.

• Impact😎: Work with the most powerful digital intelligence platform in the world, loved by both customers and employees.

• Innovation🚀: We value open dialogue and