Senior Data Scientist, Product, Real World Journeys, Search

Key Skills

Python Programming, SQL, Data science techniques, R Programming, Effective communication skills

Job Description

Minimum qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, a related quantitative field, or equivalent practical experience.
• 5 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).
• Excellent coding skills, and ability to understand and update analysis code.
• Excellent problem framing, problem-solving and team collaboration skills.
• Excellent communication skills, especially in communicating technical and statistical concepts clearly and concisely among audiences at many different levels.

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

• Frame and solve ambiguous business problems and communicate the result. Interact cross-functionally.
• Work cross-functionally with multiple leads from Engineering, Product, and UX to uncover new product opportunities and shape directions.
• Communicate clearly to influence leadership.
• Apply problem-solving, experimentation, and causal inference to address business challenges.
• Perform data exploration to uncover insights and drive strategy. Provide data recommendations to guide opportunities and prioritization.