Senior Business Data Scientist, Behavioral Economics (Spanish, English)

Minimum Qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 4 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 due to business needs.

Preferred Qualifications:

• PhD in a quantitative discipline such as Statistics, Computer Science, Mathematics, or a quantitative social science such as Economics, Psychology, or Political Science, or equivalent practical experience.
• Experience articulating business/research questions, designing analysis frameworks based on those questions, sourcing/creating the necessary data, using statistical analysis and hypothesis testing to arrive at an answer, and translating analysis results into business recommendations.
• Experience with analyzing machine learning model performance or working on LLMs.
• Proficiency in applying machine learning techniques to large datasets.

About The Job

As a Business Data Scientist, you will work across the organization to shape Google's business and technical strategies by creating, processing, analyzing, and interpreting huge datasets. By using investigative and statistical methods, you will use data to identify opportunities for Google to operate more efficiently, from enhancing advertising efficacy to improving our understanding of user behavior.

In this role, you will work with a multi-disciplinary team of data scientists, engineers, product managers, business associates, and marketing teams to adjust Google's practices according to your findings. You will work on multiple projects concurrently, developing datasets, analyses, models, and methods to support and verify the impact of your recommendations.

Responsibilities

• Develop metrics to track and evaluate solution deployment across teams.
• Create dashboards and tools to automate processes, generate reports, and guide product decisions.
• Curate and validate data to ensure quality standards are met.
• Collaborate with stakeholders to understand the domain, business goals, and data infrastructure context.