Data Scientist Ii, Infrastructure

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

Data Scientist II, Infrastructure role at Pinterest focused on improving the measurability, intelligibility, and actionability of the company's infrastructure systems. Responsibilities include defining and measuring infrastructure health, building analytical frameworks, strengthening data foundations, and analyzing experiments to optimize performance, reliability, and cost. Requires strong SQL, analytical programming, experimentation, and communication skills, with a focus on improving complex systems.

What you'd actually do

  1. Partner with engineering teams to define, measure, and improve the health, quality, and efficiency of Pinterest’s infrastructure systems.
  2. Build and refine metrics, dashboards, and analytical frameworks that make complex technical systems more understandable and actionable.
  3. Strengthen data foundations by improving metric definitions, auditing data quality, and contributing to pipeline and measurement improvements where needed.
  4. Design and analyze experiments, investigations, and deep dives to quantify the impact of infrastructure changes on user experience, reliability, and business outcomes.
  5. Translate ambiguous technical problems into clear analyses and actionable recommendations for engineering and platform partners.

Skills

Required

  • Masters degree in Statistics, Applied Math, Biostatistics, or equivalent experience
  • Strong SQL
  • Analytical programming skills
  • Experience working through messy, imperfect data
  • Experience building reliable metrics and datasets
  • Experience partnering on or contributing to production-ready data pipelines, measurement systems, or foundational data work
  • Solid foundation in experimentation and measurement
  • Ability to design analyses and interpret results rigorously
  • Ability to partner effectively with engineers and cross-functional stakeholders
  • Demonstrated ability to translate ambiguous problems into clear analytical workstreams and actionable recommendations
  • Strong cross-functional communication skills
  • Ability to explain technical findings clearly to engineering, product, and platform stakeholders
  • Ability to operate independently
  • Ability to prioritize across longer-term projects and fast-turn inbound requests
  • Ability to drive work forward in a dynamic environment
  • Curiosity
  • Builder mindset

What the JD emphasized

  • messy, imperfect data
  • production-ready data pipelines
  • ambiguous technical problems
  • messy systems