Business Data Scientist, Connected TV, YouTube Marketing

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. YouTube Marketing Analytics is a 30+ analyst team that influences and informs YouTube’s marketing and products teams. We work on projects that are at the heart of go-to-market (GTM) and brand strategy for YouTube using data to ensure our marketing efforts are efficiently and effectively deployed. As a Business Data Scientist on YouTube Connected TV (CTV) marketing, you will be tasked with understanding the full funnel of subscriber growth and determining how marketing can be the most effective at each stage in driving growth. Measurement will include a mix of understanding brand perception and how marketing shifts perception to experiment design as well as analysis of campaigns, user level offers and partnership opportunities.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
• Experience with statistical methods including experimental design (A/B testing), regression, and classification.

Preferred qualifications:

• 5 years of experience in the consumer tech, media, or entertainment industry.
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
• Experience with advanced modeling techniques, including marketing mix modeling (MMM), user LTV forecasting, and churn prediction.
• Experience in subscription-based businesses (SaaS) or mobile growth marketing.
• Deep expertise in causal inference techniques and a proven track record of applying them to solve business problems.
• Excellent communication and presentation skills, with a knack for distilling complex topics into simple, powerful messages.

Responsibilities

• Partner with performance marketing teams to design experiments and utilize causal inference and other quasi experiments techniques to measure and optimize budget for acquiring high-value subscribers.
• Develop sophisticated user segmentation frameworks to tailor marketing communications, offers, and win-back strategies to different user needs.
• Measure brand love using panel data, understand the causal drivers of brand perception and how marketing influences perception and consideration for YouTube TV and NFLST.