5 years of experience in data science or quantitative analytics with focus on statistical modeling, machine learning, and visualization.

Experience delivering bespoke analytics to senior stakeholders (e.g., problem scoping/definition, modeling, interpretation, presentation).

Experience working with human evaluation data such as surveys and large datasets in a distributed system.

Excellent written and verbal communication and presentation skills.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

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 ML, Systems and Cloud AI (MSCA), we are uniquely positioned to build best data practices throughout Google, working with teams like Google DeepMind (GDM) and Cloud AI, and push the frontiers of GenAI. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $161,000-$239,000 + 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 large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
  
• Present analysis to relevant stakeholders and executives in order to share insights, influence product direction and answer complex questions regarding data quality measurement and impact on model performance.
  
• Build and prototype analysis pipelines iteratively to provide insights at scale. Work closely with product teams to incorporate important analysis into existing framework and tools.
  
• Interact cross-functionally with a wide variety of product and model teams. Work closely with data scientists and other engineers to identify opportunities for, design, and assess improvements of data quality and model performance.