Job Description

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  

Preferred qualifications:

• 5 years of experience in a data-driven environment, specifically in building and deploying investigative solutions at scale.
• Ability to write complex SQL and use Python to build data pipelines, analyze data, and develop forecasting models (statistical or ML).
• Ability to influence executive stakeholders and manage complex projects with minimal oversight.
• Ability to work flexible hours aligned with North America.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

Responsibilities

• Lead the strategy, development, and implementation of complex forecasting models, and Google-standard code for critical systems.
• Own the functional requirements and data pipelines for critical operational metrics, either directly or through oversight of supplier resources.
• Act as a technical mentor for statistical methods and other investigative solutions and provide technical oversight to unblock automation goals.
• Drive problem framing for ambiguous and complex programs, build new processes with the foresight to address future business challenges.
• Architect, develop, and deploy scalable forecasting and Machine Learning (ML) solutions using Python, and drive the adoption of Google-standard coding practices to ensure robust, repeatable analyses that optimize global vendor operations.