Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 5 years of experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL), or 2 years of experience with a Master's degree.

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• Experience with developing machine learning models (supervised and unsupervised), Launch Experiments (A/B Testing), end-to-end Data infrastructure and Analytics pipelines.
• Experience with classification and regression, prediction and inferential tasks, training/validation criteria for machine learning algorithm performance.
• Experience in identifying opportunities for business/product improvement and defining the initiatives.
• Experience in developing new models, methods, analysis and approaches.

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

• Partner with Play/Apps Product Managers, Engineering, UX and other Play teams to understand user behaviors, design and analyze experiments and drive product insights to improve user experiences.
• Act as a thought partner to produce insights and metrics for various technical and business stakeholders across the Play/Apps team.
• Deliver presentations of findings and recommendations to multiple levels of leadership, creating visual displays of quantitative information.
• Follow engineering best practice to create data pipelines and models.
• Develop and automate reports, build and prototype dashboards to provide insights for problem-solving needs.