Senior Staff Data Scientist Manager, Research, NetSoft

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 10 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
• 2 years of experience managing a team of analysts, data scientists, or product analysts.

Preferred qualifications:

• 12 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 years of work experience with a PhD degree.
• 6 years of experience as a people manager within a technical leadership role.
  

About the job

NetSoft's mission is to enable agile planning, optimized design, and rapid deployment cycle times for Google production networks.

The team works on a varied set of business-critical software systems ranging from developing Network Risk assessments, Failure modeling and Network-Demand generation systems. In particular, the areas of Risk and Forecasting both require experienced scientists with deep industry experience, who can provide thought-leadership and strategic thinking.

Network Risk measurement is specially an area that is filled with deep problems that are critical for measuring and baselining the product requirements both from internal and external perspective.

The US base salary range for this full-time position is $248,000-$349,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

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). Independently format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.