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.The US base salary range for this full-time position is $163,000-$237,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Architect automated pipelines and scalable dashboards (e.g., Creative Diagnostics) as the global product creative source of truth. Deliver reusable, scaled solutions that automate OKR tracking and optimize sales enablement and development processes.
- Translate ambiguous business questions into tractable analytical frameworks. Synthesize complex data into actionable strategic recommendations that influence roadmaps Product teams.
- Quantify the creative business through customer sophistication and segmentation models. Forecast growth and identify key value drivers to prioritize high-impact work and refine long-term strategic parameters.
- Partner with Product, GTM, Sales, and Finance to integrate findings into business cases. Win stakeholder trust by proactively identifying product gaps and providing quantitative feedback for strategic planning.
- Take ownership of projects from conception to completion, managing the full project lifecycle, ensuring timely delivery, and maintaining clear communication with all stakeholders.
Qualifications
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.