Minimum qualifications: 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 3 years of work experience with a PhD degree. Experience with data extraction and manipulation, experimental design, statistical analysis, predictive modeling and machine learning. Preferred qualifications: PhD in Statistics, Mathematics, Data Science, Economics, or a related quantitative field. 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree. 4 years of experience, including expertise with statistical data analysis on real-world data such as causal inference methods, ML modeling, and experimentation. Experience articulating and translating business questions, using statistical techniques to arrive at an answer using data. Ability to demonstrate skills in selecting the right methodology given a data analysis problem. About the job The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Poland: zł340000 - zł349000 (PLN) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Collaborate with stakeholders in cross-project and team settings to identify and clarify critical business or product questions, providing expert feedback to translate and refine these questions into tractable analyses, evaluation metrics, or rigorous mathematical models. Design and evaluate models to mathematically express and solve defined problems with limited precedent. Leverage specialized knowledge of custom data infrastructure and existing data models to improve product performance. Drive AI product success by owning the end-to-end data science life-cycle for features, measuring and optimizing AI experience impact to ensure Google’s innovations deliver tangible user value. Leverage deep knowledge of ML/statistical approaches and causal inference to expertly develop inference algorithms and estimate user incremental lift.