Pinterest is seeking to advance its Curation ecosystem by developing models and methodologies to explain content promotion dynamics and influence product development, ultimately enhancing the personalized experience for millions of users worldwide.
Requirements
- 7+ years of experience analyzing large-scale data in fast-paced, data-driven environments
- Proven ability to apply scientific methods to solve real-world problems
- Strong interest and experience in recommendation systems and causal inference
- Proficiency in quantitative programming languages such as Python or R
- Expertise in data manipulation and query languages including SQL and Spark
Responsibilities
- Develop a comprehensive understanding of Pinterest’s Curation ecosystem, including boards, trends, collages, and user behaviors
- Design and implement robust frameworks combining online and offline methodologies to analyze recommendation outputs
- Apply scientific rigor and advanced statistical techniques to inform product creation, development, and enhancement
- Generate hypotheses, conduct experiments, and interpret results to improve recommendation relevance and user engagement
- Identify opportunities to leverage data for optimizing content promotion and Pinner experience
- Mentor junior data scientists, fostering a culture of continuous learning and technical excellence
- Focus relentlessly on impact, whether through strategic insights, metric improvement, or process enhancements
Other
- Ability to independently manage projects and drive results
- Excellent written and verbal communication skills for technical and non-technical audiences
- Team-oriented mindset with a collaborative approach to problem-solving
- Bachelor’s or Master’s degree in computer science, data science, statistics, or a related field
- Flexible working arrangements, including a hybrid model with in-office collaboration 1-2 times per quarter
