Senior Data Scientist, Google Cloud
10 Months ago • 5-8 Years
Job Description
As a Senior Data Scientist at Google Cloud, you'll work on scalability and storage solutions, large-scale applications, and entirely new platforms for developers worldwide. You'll analyze various client and server-side logs, define key metrics, and apply rigorous statistical and machine learning methodologies to generate actionable insights. You'll collaborate with engineering and product management teams developing GenAI-based development tools critical to developer productivity. This involves working with a conversational AI tool that enables users to collaborate with generative AI, augment their imagination, expand their curiosity, and enhance their productivity. Your responsibilities will include collaborating with stakeholders to identify and clarify business or product questions, translating business questions into tractable analysis, evaluating models to solve defined problems, and gathering, extracting, and compiling data across sources.
Good To Have:
- PhD in a quantitative discipline
- 8 years of work experience using analytics
- Experience with GenAI-based development tools
Must Have:
- Master's degree in a quantitative field
- 5 years of work experience using analytics
- Coding (e.g., Python, R, SQL)
- Querying databases or statistical analysis
- Collaborating with stakeholders
- Translating business questions into analysis
- Gathering, extracting, and compiling data
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
- 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
Preferred qualifications:
- PhD in a quantitative discipline such as Statistics, Engineering, Physics or Mathematics, or a related field.
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
About the job
We collaborate closely with engineering and product teams, and our impact is substantial across many areas as Google Cloud continues its rapid expansion. Our projects include building experiment frameworks for users and resources, understanding user engagement, and optimizing product resource usage and efficiency. To understand our users, we analyze various client and server-side logs, define key metrics, and apply rigorous statistical and machine learning methodologies to generate actionable insights.
As a data scientist, you will work on scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. You will evaluate and improve Google's products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. You will bring scientific excellence and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of the end user.
In this role, you'll partner with engineering and product management teams developing GenAI-based development tools critical to developer productivity.
A conversational AI tool that enables users to collaborate with generative AI and help augment their imagination, expand their curiosity, and enhance their productivity.
Responsibilities
- Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
- Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
- Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
- Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
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