Data Scientist, Research, Search AI Answers Model Quality (English, Portuguese)

Key Skills

Data science techniques, Effective communication skills, Python Programming, R Programming, SAS, SPSS Statistics, SQL, Stata

Job Description

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
• Ability to communicate in English and Portuguese fluently to support client relationship management in this region.

Preferred qualifications:

• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
• 4 years of experience analyzing data and creating reports with strong statistical, quantitative modeling, and forecasting skills.
• 3 years of experience in an applied research setting.
• Experience in survey research, excellent working knowledge of statistics, and proficiency in one or more statistical software packages (e.g., R, SPSS, Stata, SAS).
• Experience with Python, R, or other scripting languages.

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

Our team develops and supports evaluation methodology and metrics used to guide model iteration, improve training data, and measure product quality for AI related Search features (e.g., Search AI Overviews). We are a group of data scientists who work in close collaboration with engineers, researchers, and Product Managers from Search and Google DeepMind to bring helpful and trustworthy generative content to Search users.

As an AI Answers Model Quality Data Scientist, you will be a part of the Search Data Science team, whose mission is to drive product excellence and user and ecosystem growth for Search with data insights.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.