Data Scientist III, Research, Privacy Sandbox
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Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
- 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
Preferred qualifications:
- Ph.D. or Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
About the job
The Privacy Sandbox Data Science team (Kurtosis) supports Privacy Sandbox by ensuring user's activity private across a free and open Internet. The central data science problem in Privacy Sandbox is quantifying the tradeoffs between privacy and web monetization, so that we can ensure measurable progress in enhancing user privacy while also not taking away the monetization pathways that allow for a vibrant, open Internet, without content locked behind paywalls. Anticipated projects include, categorizing breakage on the web to ensure that privacy-sensitive users aren’t getting a broken browsing experience because of their privacy choices. Developing and validating new experimentation methodology that will work with the limited signals available in anonymized data.
Responsibilities
- Leverage advanced statistical methods on massive, complex datasets to extract insights from billions of events and thousands of features across organizational sources.
- Develop and deploy automated solutions, ranging from SQL query automation to real-time Python classification and ML modeling, to address key tactical issues.
- Analyze intricate product and platform usage patterns, translating data-driven insights into actionable product strategy and engineering decisions.
- Demonstrate proficiency in technical and methodological conversations, as well as narrative-driven presentations.
- Possess a deep interest and aptitude for data, metrics, analysis, and trends, with applied knowledge of measurement, statistics, and program evaluation.
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