Data Scientist , Research, AI Data, Data Science

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 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.

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.
• Experience in deep learning methods or large language models.

About the job

AI Data Data Science works on some of the most exciting data quality problems at Google, including those in the GenAI/LLM space. We are a customer-focused team, supporting Machine Learning teams throughout Google and partnering with data teams affiliated with the AI Data organization. We will are looking for new team members who are excited about GenAI and the paradigm shift that it brings, can apply aptitude to novel and challenging problems, want to think through the various important processes and communications efforts to make this style of team work as well as possible.

We believe that high quality data is key to building better AI models, especially in the era of Large Language Models (LLMs). We work directly with model and product teams to measure and improve data quality, collect and generate high quality data, develop evaluation methodologies, and enhance model performance. As a part of AI Data, we are positioned to build best data practices throughout Google, working with teams like Google DeepMind (GDM) and Cloud AI, and push the frontiers of GenAI.
The US base salary range for this full-time position is $127,000-$187,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Work with data sets. Solve analysis problems, applying investigative methods. Conduct analysis that includes data gathering, requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
• Present analysis to stakeholders and organization executives in order to share insights, influence product direction and answer questions regarding data quality measurement and impact on model performance.
• Build and prototype analysis pipelines iteratively to provide insights at scale. Work with product teams to incorporate important analysis into existing framework and tools.
• Interact with a wide variety of product and model teams. Work with engineers to identify opportunities for, design, and assess improvements of data quality and model performance.
• Define key metrics that are statistically sound and meaningful to measure data quality for data in various shapes and forms, as well as to measure progress of customer engagement.