Senior Staff Data Scientist Manager, AI Data

Google Google · Big Tech · Mountain View, CA +1

This role focuses on improving the quality of data used for training Large Language Models (LLMs) and other ML models. Responsibilities include analyzing large datasets, defining data quality metrics, researching methods to optimize data quality, and influencing product direction based on data insights. The role involves working with data for ML model training and data acquisition spend optimization.

What you'd actually do

  1. Work with large, data sets. Conduct analysis that includes data gathering and requirements specification, processing, cleaning and curation, analysis, visualization, ongoing deliverables, and presentations.
  2. Represent analysis to stakeholders and organization executives in order to share insights, influence product direction and answer difficult questions regarding data quality measurement and impact on model performance.
  3. 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.
  4. Research and develop analysis and optimization methods to improve the quality of Google's ML portfolio and applications, including LLM model and training data planning.
  5. Conduct independent research and advance the state of understanding in how data impacts ultimate quality of large language models and creating spend optimization priorities with data acquisition.

Skills

Required

  • Statistics
  • Data Science
  • Mathematics
  • Physics
  • Economics
  • Operations Research
  • Engineering
  • Python
  • R
  • SQL
  • data analysis
  • data gathering
  • requirements specification
  • data processing
  • data cleaning
  • data curation
  • data visualization
  • statistical analysis

Nice to have

  • people management
  • technical leadership

What the JD emphasized

  • high quality data is key to building better Machine Learning (ML) models
  • data quality
  • model performance
  • training data planning
  • data acquisition

Other signals

  • data quality for LLMs
  • data acquisition
  • ML data engineering
  • model performance