Internship Google Play Store Jobs (NOW HIRING)

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X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; San Francisco, CA, USA.
Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in a technical project management or a customer-facing role.
• 3 years of experience managing projects from inception to completion within a global organization.
• 3 years of experience in human computation or data labeling.

Preferred qualifications:

• Experience working with product and engineering teams.
• Experience in designing, implementing and managing product development and operationalization frameworks and methodologies.
• Experience with prompt engineering and human computation.
• Ability to make smart tradeoffs and rapidly launch and iterate new product features for a user-facing commercial organization.
• Excellent problem solving skills, with the ability to analyze research or performance data, develop insights and apply them to optimize the product roadmap.

About the job
Play's Store Product Solutions and Operations team strives to enable exceptional user experiences within the Google Play Store by enabling new features and delivering high-quality, trusted data that powers Store treatments. In this critical role, you will work cross-functionally to manage, scale, and enhance our solutions, utilizing a sophisticated blend of human insights and machine learning.
As Product Solutions and Operations Manager, you'll partner with a global, cross-functional team comprising product managers, engineers, and product specialists, to provide Google Play users with the best store experience every time they come to Play.
In this role, you will be responsible for ensuring our program produces reliable labeled data, by designing, running, and owning human computation tasks across our international rater team. You'll collaborate with experts across Google Play to design evaluations, deploy tools and processes to our international raters, analyze data, and drive ongoing process improvements. You'll have ownership over critical product data streams and work with your product and engineering partners to prioritize human computation projects, the best way to collect data, and how much data will be required. This data feeds into the systems that determine the best apps and games to show to individual users, and also contributes to training machine learning models and evaluating new product launch decisions.
Our team enables Play users to have the very best experience with the Google Play Store, connecting each individual to the best apps and games on their devices, and you will use your bias for action and passion for incredible mobile user experience to join us.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $114000 - $164000 (USD) 15% bonus target bonus equity benefits
Learn more about benefits at Google .
Responsibilities

• Collaborate with Product Management, Engineering and Product Marketing teams to launch new products, scale operations, surface critical data signals to increase user engagement and improve developer Return on Investment (ROI).
• Own product data streams, partnering with subject matter experts to implement evaluations to production and collect data from rater teams at scale.
• Track data quality and operational Key Performance Indicators (KPIs) and perform analysis to drive continuous improvement and troubleshoot issues.
• Work closely with global vendor raters, vendor managers and vendor trainers to identify gaps or issues in data collection, and design and implement solutions to address them.
• Stay up to date on the latest data labeling methods, including human rater labeling and machine learning, and make recommendations for LLM-first solutions to scale output.

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