Data Scientist, Product, Core Developer (English)

Key Skills

Data science techniques, Python Programming, R Programming, SQL

Job Description

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related field or equivalent practical experience.
• 2 years of work experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or experience with a Master's degree.
• Ability to communicate in English fluently to support client relationship management in this region.

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 3 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
• Experience working in a development life-cycle, defining requirements, checking in code, testing, staging, production.
• Experience with analytics, data, and measurement products.

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.

In this role, you will partner with our Engineers/Program Manager (PM)/UX team members to advance Google GenAI capabilities, leading industry productivity research and AI impact, enhance complex engineering development ecosystems, and help leadership make launch and prioritization decisions.

Responsibilities

• Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Apply standard/common tools, resources, and processes to define issues for difficult projects, execute problem-solving 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 business priorities and problen-solving techniques.
• Select appropriate approaches from options to address technical issues.
• Develop working relationships outside of the team to contribute to cross-project collaborations and better understand data or products.