Data Scientist Ii, Experimentation

Pinterest Pinterest · Consumer · San Francisco, CA · Data Engineering

Data Scientist II, Experimentation role at Pinterest focused on improving and iterating on the experimentation platform. This involves researching methodologies, streamlining experiment setup/evaluation, creating workflows for experimentation metadata, and consulting with product data science teams. Requires a strong background in experimentation, statistical analysis, and coding best practices.

What you'd actually do

  1. Comb through the literature in experimentation to identify potential methodologies that can improve parts of our platform where we have the biggest opportunities.
  2. Make the process of setting up, running and evaluating experiments smoother and more repeatable for our platform users, ensuring that decisions are risk-aware and consistent
  3. Write workflows to make our vast experimentation meta-data able to be leveraged by our team and outside of our team to better understand the experimentation landscape.
  4. Consult with product data science teams to debug, design or improve their experiments and experimentation process.

Skills

Required

  • Experimentation
  • Statistical Analysis
  • Data Science
  • Software Development Best Practices
  • Version Control (Git)
  • Workflow Management Tools (Apache Airflow)

Nice to have

  • PhD in stats, applied math, biostatistics
  • State of the art experimentation methodologies

What the JD emphasized

  • PhD in a relevant field (stats, applied math, biostatistics, etc…) OR 2+ years of hands-on experience working as a data scientist or applied scientist.
  • Experience working directly on experimentation problems and an awareness of state of the art methodologies.
  • Proficiency in software development best practices, including version control systems such as Git, to ensure efficient collaboration, code management, and reproducibility in a data science environment.
  • Familiarity with workflow management tools such as Apache Airflow to create and schedule data pipelines, allowing for automated and reliable execution of machine learning workflows.