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.

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.

Minimum qualifications:

• Bachelor’s degree in Statistics, Computer Science, Mathematics or a related field and 5 years of progressive, post-baccalaureate experience in the job offered or in a Data Scientist-related occupation.
• Alternatively, will accept a Master’s degree in Statistics, Computer Science, Mathematics or a related field, and 3 years of experience in the job offered or in a Data Scientist-related occupation.
• Position requires 3 years of experience in the following: SQL for data extraction and manipulation; Python or R for statistical analysis and problem-solving; Applying statistical methods including t-tests and descriptive statistics; Defining and rationalizing metrics to measure business outcomes; and Communicating analytical findings and statistical concepts to stakeholders, including senior leadership.