Senior Data Scientist, Research, Reliability Analytics

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:

• 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.
• Knowledge of C++.

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google’s business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google’s practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

Reliability Analytics is a team of data scientists that works closely with engineering teams in Google’s reliability organization to quantify and improve reliability of Google’s technical infrastructure. This involves a broad spectrum of reliability problems where data-driven approaches can be applied. We aim to combine subject matter expertise with statistical or machine learning methods to improve reliability in ways that matter to Google’s users and customers.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google’s product portfolio possible. We’re proud to be our engineers’ engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Responsibilities

• Collaborate closely with engineering and product teams to build and improve reliability tools and products according to engineering priorities.
• Reduce manual toil and gain automatic insights in system monitoring and alerting using state-of-the-art statistical and machine learning methods, resulting in more accurate and faster triage of reliability issues, and thereby reducing time to mitigation.
• Collaborate with other data scientists that support a wide range of reliability objectives and contribute to forums for team-wide learning and development.
• Design and build tools and data pipelines for integration in existing or new reliability infrastructure and products. Design and build machine learning and data pipelines in Python, Tensorflow.
• Understand relevant engineering infrastructure and data assets relevant to reliability. Influence engineering priorities and road maps in key areas of reliability.