Data Scientist

Google•New York, NY

11d•$138,000 - $198,000•Hybrid

About The Position

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 [https://www.google.com/about/careers/applications/benefits/]. Position reports to the Google office & may allow for a hybrid schedule as per Google policy.

Requirements

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.
2 years of experience in SQL for data extraction and manipulation.
2 years of experience in Data pipeline creation for data engineering.
2 years of experience in Python or R for statistical analysis and problem-solving.
2 years of experience in Applying statistical methods including t-tests and descriptive statistics.
2 years of experience in 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.