Data Scientist, Business and Marketing, Ads Marketing Analytics
Job Summary
Job Description
- Master's degree in quantitative discipline
- 3+ years analytics experience
- Proficiency in Python, R, SQL
- Fluent in English and Spanish
- Strong communication & presentation skills
- PhD in Statistics or related field
- Experience with generalized linear models
- Experience in controlled experiment design
- Ability to teach and learn new techniques
- Excellent critical thinking skills
11 skills required
Job Details
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 teach others and learn new techniques.
- Excellent critical thinking and problem-solving skills.
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.
Google Ads Marketing aims to help advertisers of all sizes succeed with digital marketing. This is a unique opportunity to apply the tools of data science to accelerate ads business growth, working cross-functionally with Sales, Marketing and Product teams.
As a Data Scientist on the Ads Marketing Data Science team, you will work with the team to advance the science of marketing to customers that use Google’s advertising solutions. You will perform deep data analytics, drive initiatives in experimentation, measurement and advance machine learning modeling capability to support global marketing programs.You will be working with Marketing, Product, Data Science, and Engineering teams. You'll leverage data to define key metrics and generate insights for impactful marketing programs. You'll design and build analysis pipelines to support large-scale initiatives and campaigns. You will inform strategic marketing decisions across acquisition, onboarding, and growth. You will build investigative frameworks and measurement tools to drive business growth. You will communicate data-driven insights and results to marketing partners and leadership to guide decision-making.
Responsibilities
- Work with large, complex data sets. Solve complex analysis problems, applying advanced investigative methods such as statistical and machine learning models as needed. Conduct analysis that includes problem formulation, data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Design and analyze controlled experiments or counterfactual causal inference studies to examine the incremental impact of Ads marketing programs, also build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed.
- Interact cross-functionally, making business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
- Develop and automate reports, iteratively build and prototype dashboards to provide insights at scale, solving for business priorities.
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