Staff Data Scientist, Ads Metrics & Experimental Design

Google LLC

Zürich

Vor Ort

CHF 140.000 - 230.000

Vollzeit

vor 25 Stunden
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

Google LLC’s Ads Metrics team is a leading force in data science for Search Ads, driving experiment design, evaluation metrics, and advanced analytics to improve ad quality and ROI.

You will collaborate with software engineers, product managers, researchers, and analysts to push the boundaries of causal inference and time-series analysis for business-critical launches at Google's multi-billion-dollar global scale.

Qualifikationen

  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, OR/Engineering or equivalent.
  • 8 years of analytics experience solving product or business problems, or 6 years with a PhD.

Aufgaben

  • Collaborate with engineering, product, researchers and analysts to push the boundaries of experiment design, causal inference, and time-series analysis.
  • Architect end-to-end analysis pipelines translating business questions into rigorous models and metrics.
  • Design and evaluate complex experiments or models to solve problems with limited precedent in retrieval and ranking.
  • Translate system telemetry into statistically sound evidence ensuring data integrity for research-grade analysis.

Kenntnisse

Statistical analysis
Experiment design
Causal inference
Data-driven decision making
Communication

Ausbildung

Master's degree (Statistics/Data Science/Mathematics/Physics/Economics/OR/Engineering)
PhD degree in quantitative discipline

Tools

Python
R
SQL
Gemini
Antigravity

Jobbeschreibung

Google LLC’s Ads Metrics team is a leading force in data science for Search Ads, driving experiment design, evaluation metrics, and advanced analytics to improve ad quality and ROI.

You will collaborate with software engineers, product managers, researchers, and analysts to push the boundaries of causal inference and time-series analysis for business-critical launches at Google's multi-billion-dollar global scale.

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