Business Data Scientist, Ads Marketing Analytics

About the role

MINIMUM QUALIFICATIONS:

• Master's degree in a quantitative field (Statistics, Mathematics, Data

Science, Bioinformatics, Economics, etc.) or equivalent practical experience

• 3 years of experience in a data science field.
• Experience with statistical software (e.g., R, Python, MATLAB) and database

languages (i.e., SQL).

• Experience using analytics to solve product or business problems, querying

databases or statistical analysis.

PREFERRED QUALIFICATIONS:

• PhD in Statistics or related quantitative discipline.
• 2 years of experience, including 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 prioritize requests and partner well in an environment with

competing demands from stakeholders.

• Ability to convince business stakeholders and communicate analysis insights

to non-technical audiences and willingness to both teach others and learn new

techniques.

• Excellent communication and team-work including 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 helps advertisers of all sizes succeed with digital

marketing. In this role, you will work with a team to advance marketing science

for customers using Google’s advertising solutions. This unique opportunity

applies Data Science tools to accelerate Ads business growth, working

cross-functionally with Sales, Marketing, and Product teams.

As a Data Scientist on this team, you will perform deep data analytics, drive

experimentation and measurement, and advance machine learning modeling to

support global marketing programs.

Collaborating with a multidisciplinary team of marketers, product managers, data

scientists, and engineers, you will leverage underlying data to align on key

metrics and methodologies. Your insights will enable marketers to develop highly

effective programs. Using core Data Science expertise, you will design,

prototype, and build scalable analysis pipelines to support campaigns. You will

perform analytics, execute experimentation, and conduct incrementality

measurement to inform strategic decisions across the entire Ads Marketing

lifecycle—from acquisition and onboarding to growth and retention.

You will build investigative frameworks and measurement capabilities to generate

data-driven insights that drive business growth. You will effectively

communicate your investigative results to marketing partners and leadership to

inform critical decision-making.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Work with large, complex 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 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.