MINIMUM QUALIFICATIONS:
Bachelor's degree in Statistics, Mathematics, Data Science, Engineering,
Physics, Economics, or a related quantitative field.
10 years of work experience using analytics to solve product or business
problems, performing statistical analysis, and coding (e.g., Python, R, SQL)
or 8 years of work experience with a Master's degree.
PREFERRED QUALIFICATIONS:
Master's degree in Statistics, Mathematics, Data Science, Engineering,
Physics, Economics, or a related quantitative field.
13 years of work experience using analytics to solve product or business
problems, performing statistical analysis, and coding (e.g., Python, R, SQL),
working with statistical packages (e.g. R, SAS, Stata, MATLAB, etc.), or 10
years of work experience with a Master's degree.
Experience articulating product questions, pulling data from datasets
(Python, R, SQL) and using statistics to arrive at an answer using analytical
thinking and debugging skills.
Experience with databases, data warehouses, and business intelligence and
analytical tools.
Knowledge of commercial reporting tools.
ABOUT THE JOB:
We strive to understand our users, such that we can help enable an efficient and
scalable ML infrastructure, by facilitating a deep understanding through
rigorous analysis of opportunities to improve efficiency. These opportunities
can be addressed via data transparency, software stack improvements, user
engagements and service definition innovations (e.g., pricing, product tiers),
which will have a lasting impact in the alignment of our service to our product
area user needs. Your work will influence how Google spends, to cost-optimally
scale and operate ML infrastructure that spans the world, and meets the rapidly
growing needs of Google's ML products and research. You will work closely with
many stakeholders, including senior executives in Capital Engineering, Finance,
Platforms and Research, as well as product area resource management teams.
Google’s homegrown, bespoke ML TPU infrastructure is one of Google’s fastest
growing infrastructure investments, which enables increases in performance
despite the end of Moore’s Law. ML Efficiency Data Science is the team in Cloud
that provides insights, tools and analyses that help ML infrastructure service
consumers. To accomplish that, the data science team collaborates with teams
cross-functionally such as Capital Engineering, Finance, Product Managers, PMO
and executive leadership to enable the scalable, reliable, and efficient
deployment and consumption of ML compute resources across Google.
In this role, you must be highly strategic, comfortable with ambiguity and be an
exceptional communicator with a bias to action, as well as an agile and creative
problem solver and able to build strong relationships and collaborate across
functions.
Individual pay is determined by factors including job-related skills,
experience, and relevant education or training.
US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google
[https://www.google.com/about/careers/applications/benefits/].
RESPONSIBILITIES:
Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Provide
analytical thought leadership through proactive and strategic contributions
(e.g., suggests new analyses, infrastructure or experiments to drive
improvements in the business).
Own outcomes for projects by covering problem definition, metrics
development, data extraction and manipulation, visualization, creation, and
implementation of analytical/statistical models, and presentation to
stakeholders.
Develop solutions, lead, and manage problems that may be ambiguous and
lacking clear precedent by framing problems, generating hypotheses, and
making recommendations from a perspective that combines both, analytical and
product-specific expertise.
Oversee the integration of cross-functional and cross-organizational
project/process timelines, develop process improvements and recommendations,
and help define operational goals and objectives.
Oversee the contributions of others directly or indirectly, and develop
colleagues’ capabilities in the area of specialization.