Data Scientist, Product, Identity Personalization

2 weeks ago

Tel Aviv, Tel Aviv, Israel Google Full time
Minimum qualifications:
  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or 2 years of work experience with a Master's degree).
Preferred qualifications:
  • Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, Computer Science, or a related quantitative field.
  • Experience in framing and solving unstructured business problems with data science, translating results into impactful business recommendations, and measuring the success of those initiatives.
  • Experience with LLM evaluation techniques.
  • Experience with human evaluation data such as surveys and raters.


About the job

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.



Responsibilities
  • Perform large-scale statistical modeling and user-journey analysis to identify product opportunities and quantify the impact of initiatives on Key Performance Indicators (KPIs).
  • Collaborate with Engineering, Product, and UX teams to develop validation methodologies, evaluation metrics, and fine-tuning datasets for Large Language Models.
  • Define and implement robust data quality frameworks while automating dashboards and reports to provide actionable insights at scale.
  • Lead advanced experimentation (including user learning effects) and present data-driven recommendations to cross-functional stakeholders to drive product strategy.

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