Data Scientist, Product, Cloud Supply Chain and

**Minimum qualifications**:

- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 5 years of experience with statistical data analysis, data mining, and querying (e.g. SQL).
- 3 years of experience managing analytical projects.

**Preferred qualifications**:

- PhD in Information Systems, Operations Research, Computer Science, Mathematics, Statistics, or Engineering.
- Experience influencing and leading organizational change.
- Deep interest and aptitude in data, metrics, analysis and trends, and applied knowledge of measurement, statistics, and program evaluation.
- Understanding of statistical foundation especially in approaches and methods related to hypothesis testing, experimentation, and causal inference.
- Business intuition with ability to synthesize multiple data points, points of view, and analyses into actionable and meaningful insights.
- Distinctive problem-solving skills, impeccable business judgment, practical analysis mindset, and comfortable with both statistically driven analysis and approaches based on the need of problem.

**About the job**:
The mission of Cloud Supply Chain Operations (CSCO) Data Science team is to improve CSCO efficiency through applied machine learning and prescriptive insights. Efficiency could come in the form of cost-savings, reduced cycle time, reduced toil on GSO users, and improved supply/demand predictability. Our data science modeling and analytics work also focuses on projects that increase satisfaction of the users across GSO, elevate our measurement capabilities, improve key business metrics. We recently inherited broader scope to support end-to-end supply chain and operations managed by Cloud Supply Chain and Operations organization. In this role, you will craft prescriptive insights, follow through to ensure recommendations are being implemented, and analysis informs a business and/or product change.

**Responsibilities**:

- Develop machine learning, statistical, and optimization models to improve supply chain and operations efficiency. Deliver difficult analytical problems with initial guidance structuring approach and conduct exploratory data analyses, inform model design, and development.
- Analyze users, usage, trends and relevant dimensions, providing insights on changing dynamics. Prioritize multiple projects and refine timelines with stakeholders.
- Plan and execute prioritized project work, including selecting appropriate methods and advising on opportunities to improve data infrastructure. Identify and recommend ways to improve solutions to problems via selecting better methods/tools.
- Identify issues with scope, data, or approach. Escalate issues to be addressed by stakeholders and communicate, present insights, and recommend actions to stakeholders.
- Be capable of independent end-to-end delivery of data extraction and manipulation, visualization, and development of analytical/statistical models. Influence logging and navigate the teams’ technical stack.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.