Data Scientist

Pinterest Pinterest · Consumer · San Francisco, CA · Engineering, Product and Design (L2)

Data Scientist role at Pinterest focused on applying quantitative modeling, experimentation, and algorithms to solve complex product and business challenges. The role involves developing best practices for instrumentation and experimentation, bringing scientific rigor to product development, building analysis pipelines for insights at scale, and collaborating cross-functionally to drive product improvements. Experience with ML/DL frameworks and large dataset manipulation is required.

What you'd actually do

  1. Develop best practices for instrumentation and experimentation and communicate those to product engineering teams to help us fulfill our mission - to bring everyone the inspiration to create a life they love
  2. Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners
  3. Build and prototype analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development
  4. Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

Skills

Required

  • SQL
  • Python
  • R
  • Machine Learning
  • Statistical Modeling
  • Forecasting
  • Econometrics
  • PyTorch
  • TensorFlow
  • scikit-learn

Nice to have

  • web-scale data analysis
  • deep learning frameworks

What the JD emphasized

  • 4+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data
  • Extensive experience solving analytical problems using quantitative approaches including in the fields of Machine Learning, Statistical Modeling, Forecasting, Econometrics or other related fields
  • Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow or scikit-learn
  • A scientifically rigorous approach to analysis and data, and a well-tuned sense of skepticism, attention to detail and commitment to high-quality, results-oriented output
  • Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R)

Other signals

  • Develop best practices for instrumentation and experimentation
  • Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement
  • Build and prototype analysis pipelines iteratively to provide insights at scale
  • Work cross-functionally to build and communicate key insights