Requirements:

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 in causal inference, A/B testing, statistical modeling, or machine learning.

Nice to haves:

Experience delivering meta analysis, fully automated analytics pipelines, or audience segmentation and propensity modeling.
Understanding of Bayesian approaches and modeling frameworks.
Ability to generate practical solutions for marketing analytics problems and use results to drive business change in partnership with cross-functional stakeholders.

Provide support in media strategy, measurement, and optimization that require expertise in advanced analytics work, with a special focus on applying data science to marketing and meta-analysis approaches.
Partner with internal teams in advanced analytics work including experimentation, measurement, and modeling.
Identify patterns and behaviors that are effective predictors of performance and critical drivers for a successful media plan.
Deliver customer-centric, data-driven approach, based on a people-based marketing strategy to build, segment, and test audiences for the best business results.
Develop evaluation frameworks for large-scale models, new metrics, and investigate anomalies. Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods.

Google's leadership team hand-picks thorny business challenges to work on.
Immerse yourself in data collection, draw insights from analysis, and develop compelling, synthesized recommendations.
Opportunity to communicate recommendations to senior-level executives, help drive implementation, and check back-in to see the impact of your recommendations.