Business Data Scientist, Ads Marketing Analytics

Business Data Scientist, Ads Marketing Analytics

Locations: Mountain View, CA, USA; San Francisco, CA, USA

Experience: Mid

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

XApplicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; San Francisco, CA, USA.

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.

Preferred Qualifications:

• PhD degree in Statistics or related quantitative discipline.
• 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 in controlled experiment design and causal inference methods.

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 perform deep data analytics, drive initiatives in experimentation, measurement and advance machine learning modeling capability to support global marketing programs. In collaboration with a multidisciplinary team of marketing, product management, data scientists and engineers, you will tap into the underlying data, develop and align on key metrics/methodologies and generate insights that enable marketers to develop powerful, highly effective marketing programs. You will leverage core Data Science expertise to design, proto@type and build out analysis pipelines to support initiatives and Marketing campaigns, perform analytics, design and execute on experimentation, and conduct incrementality measurement analysis to inform on key decisions of the marketing programs across the entire Ads Marketing space, from acquisition, onboarding and growth.

The US base salary range for this full-time position is $141,000-$202,000 + 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.

Responsibilities:

• Work with large data sets. Solve 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.
• Build and proto@type analysis pipelines iteratively to provide insights. 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 proto@type dashboards to provide insights, solving for business priorities.

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