Data Scientist II, Infrastructure
About this role
Pinterest seeks a Data Scientist II to enhance its infrastructure by collaborating with engineering teams. This role involves developing metrics, improving data quality, and analyzing infrastructure impacts on user experience and business outcomes.
What you'll do
- Collaborate with engineering to assess and enhance infrastructure health and efficiency.
- Develop metrics, dashboards, and frameworks for better technical understanding.
- Auditing data quality and refining metric definitions.
- Conduct experiments to evaluate infrastructure changes on performance.
- Translate complex technical issues into actionable insights.
- Support investigations on infrastructure performance and measurement quality.
What they're looking for
- Master's degree in relevant field.
- Proficient in SQL and analytical programming.
- Experience with data pipelines and measurement systems.
- Strong foundation in experimentation and measurement.
- Ability to translate complex problems into analytical workflows.
- Excellent cross-functional communication skills.
- Self-sufficiency in prioritizing projects and requests.
- Curiosity and a proactive mindset towards improving systems.
Benefits
- Flexible work environment.
- Opportunities for professional growth.
- Collaborative work culture.
- Involvement in innovative projects.
- Support for personal development.
Opens the official application on the employer’s site. No login required.
Pinterest builds a large-scale platform serving millions of users, with infrastructure spanning security, database systems, and mobile products, supported by data systems and advertising technology. The company is hiring Software Engineers II and experienced engineers across security, infrastructure, iOS development, and data engineering to enhance platform capabilities, improve detection and response systems, optimize performance, and leverage AI-driven solutions.
- Website
- pinterest.com
Likely interview questions
- Walk us through a time when you had to work with messy or imperfect data. How did you identify and address data quality issues, and what was the impact?
- Describe your experience building or contributing to production-ready data pipelines or measurement systems. What challenges did you face and how did you ensure reliability?