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.
- Ability to communicate in English and Spanish fluently to interact with local stakeholders.
Preferred qualifications:
- PhD degree in Statistics, or a related quantitative discipline.
- 6 years of experience with statistical data analysis such as generalized linear models, multivariate analysis, clustering/segmentation and sampling methods.
- Experience in controlled experiment design and causal inference methods.
- Ability to mentor others while learning new techniques.
- Ability to think logically and solve problems effectively.
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.
As a Data Scientist on the Ads Marketing Data Science team, you will advance marketing science for Google's advertising solutions by driving analytics, experimentation and machine learning modeling. You will leverage data to define key metrics, generate insights and inform strategic marketing decisions across acquisition, onboarding and growth. You will design analysis pipelines, build measurement tools and create frameworks to support global initiatives and business growth, while communicating data-driven insights to marketing partners and leadership to guide decisions.
Responsibilities
- Manage large, complex data sets and solve analysis problems by applying advanced methods like statistical and machine learning models and conducting end-to-end analysis from problem formulation to deliverables and presentations.
- Design and analyze controlled experiments or causal inference studies to assess the impact of Ads marketing programs, build and prototype analysis pipelines to deliver insights at scale and develop knowledge of Google data structures and metrics to advocate for necessary changes.
- Collaborate cross-functionally to provide business recommendations (e.g., cost-benefit, forecasting, experiment analysis) and present findings effectively to stakeholders through visual displays of quantitative information.
- Develop and automate reports, build and prototype dashboards iteratively to provide insights at scale, solve for business priorities.