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.
The YouTube Ads Go-to-Market (GTM) Creative team works at the intersection of data, creativity, and advertising. Our mission is to enable advertisers to grow the variety and quality of their ads, increasing their effectiveness on our platforms. We achieve this by augmenting their creative process with solutions developed by leveraging first-party and third-party approaches. Utilizing statistical models, advanced data science and analytical techniques, and AI tools, we develop insights that inform business decisions and provide clear creative guidance. Our team is responsible for foundational research and best practices, such as YouTube's ABCDs of creative effectiveness, which help our partners succeed and grow on the platform.
As a Business Data Scientist on the YouTube Ads GTM Creative Research and Data Science team, you will serve as a primary technical and analytical driver for the GTM and Sales team globally. Utilizing predictive modeling, causal inference, and experimental design, you will be instrumental in developing the insights that shape how advertisers build their brands on YouTube while building the scalable Business Intelligence infrastructure that allows global stakeholders to self-serve and track performance in real-time.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $237000 (USD) + 15% bonus target + bonus + equity + benefits
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benefits at Google.
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:
- 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience in advertising effectiveness, market research, brand strategy, or a related field.
- Experience in framing ambiguous problems and solving them using appropriate quantitative methods.
- Experience with machine learning models and AI techniques.
Ability to work with large datasets and draw conclusions from data.
- Excellent communication skills, with the ability to explain statistical and domain concepts to non-experts.