Senior Data Scientist, Product, App Ecosystem and Trust

corporate_fareGoogleplaceSingapore

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 5 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.
  
• 4 years of work experience, including expertise in translating product and business questions into clearly framed analytical projects, extracting and manipulating large datasets, and applying statistical methods to arrive at answers.
• Knowledge of data, metrics, analysis, and trends, with an aptitude for applied measurement, statistics, and program evaluation.
• Ability to manage multiple assignments simultaneously in a changing environment.
• Ability to present to executives and communicate with technical leadership teams.
• Ability to address and take ownership of large, problems.

About the job

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

Our team's mission is protecting billions of Android users from abusive applications, including malware, content abuse, impersonation, and behavioral abuse. Preventing abuse is fascinating technically and it features an adversarial scenario where we try to detect bad actors who try in turn to evade detection. Impact and success translates directly to keeping the Android ecosystem safe and protecting users from a variety of types of abuse.

As a Data Scientist, Product on App and Ecosystem Trust team, you will work with multiple stakeholders across product, engineering and operation teams to define metrics and deliver actionable insights to drive product and engineer key decisions. Analysis will span ecosystem impact, policy compliance, review efficiency and model efficacy. You will be responsible for translating data into meaningful recommendations and, where relevant, implementing process improvements. You will work collaboratively across functions including Product Management, Engineering, Operations and Program Management.

Android is Google’s mobile operating system powering more than 3 billion devices worldwide. Android is about bringing computing to everyone in the world. We believe computing is a super power for good, enabling access to information, economic opportunity, productivity, connectivity between friends and family and more. We think everyone in the world should have access to the best computing has to offer. We provide the platform for original equipment manufacturers (OEMs) and developers to build compelling computing devices (smartphones, tablets, TVs, wearables, etc) that run the best apps/services for everyone in the world.

Responsibilities

• Develop, own and evolve analytical frameworks providing a source of truth measures for the organization.
• Work closely with stakeholders in Product Management, Engineering and Operation teams to define performance goals, Key Performance Indicators and other success measurements.
• Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables and presentations.
• Conduct in-depth revealing opportunities and risks, enabling the team to understand trends and prioritize resources.
• Work with large, internal data sets, solve difficult, non-routine analysis problems, applying advanced analytical methods as needed.