Data Scientist, Product, Insights and Analytics

Key Skills

Account Management, Data science techniques, Effective communication skills, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Python Programming, R Programming, SQL

Job Description

Minimum qualifications:

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

Preferred qualifications:

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

Responsibilities

• Structure high-level business questions into data-focused investigative hypotheses.
• Write queries and perform analysis to answer business questions (SQL, R, Python).
• Develop and automate reports, iteratively build dashboards to provide insights at scale, solving for business priorities.
• Apply Large Language Model (LLM) and other Machine Learning (ML) models to analyze structured and unstructured data (eg. for natural language processing).
• Protect the team against overreaching statistical conclusions (e.g., sampling, correlation/causation).