Data Scientist, Product, Android Growth at Google Jobs In Kenya
About the job
Minimum qualifications:
- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 1 year of experience with statistical data analysis, data mining, and querying (e.g., SQL).
Preferred qualifications:
- 3 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL).
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.
- Google is an engineering company at heart. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on users around the world. At Google, engineers not only revolutionize search, they routinely work on scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, social to local, Google engineers are changing the world one technological achievement after another.
Responsibilities
- Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Apply standard/common tools, resources, and processes to defined problems and for moderately difficult projects, execute tasks with guidance.
- Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, and validate data to ensure quality.
- Leverage a foundational understanding of broader business priorities and analytical techniques.
- Select appropriate approaches from clear options to address technical challenges.
- Develop working relationships outside of the team to contribute to cross-project collaborations and understand data or products.