Data Scientist

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $138,000 - $198,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about
benefits at Google
.
Position reports to the Google office & may allow for a hybrid schedule as per Google policy.

Minimum qualifications
Bachelor’s degree in Statistics, Computer Science, Mathematics, or a related field and 2 years of experience in the job offered or in a Data Scientist-related occupation.
Position requires 2 years of experience in the following: SQL for data extraction and manipulation; Data pipeline creation for data engineering; Python or R for statistical analysis and problem-solving; Applying statistical methods including t-tests and descriptive statistics; and Performing root cause analysis using quantitative methods.

Responsibilities
Manage the end-to-end data lifecycle by gathering, extracting, and compiling data from various sources using tools like SQL, R, or Python while ensuring data quality and security.
Conduct in-depth analysis and design statistical models to interpret complex datasets, formulate conclusions, and iteratively build prototypes that provide insights at scale.
Collaborate with stakeholders to define and refine key business questions, developing an analytical roadmap and methodology to address product or business challenges effectively.
Translate complex analytical findings into actionable insights and strategic recommendations to influence product development decisions and long-term business strategy.
Present key performance indicators and analytical results to support business reviews and communicate opportunities to cross-functional leadership teams. Develop comprehensive knowledge of internal data structures and advocate for infrastructure improvements to enhance data quality and processing capabilities for future analyses.