Pinterest is looking to advance its Curation efforts by developing methods and models to explain content promotion decisions and pioneer zero-to-one product initiatives.
Requirements
- Proven ability to apply scientific methods to solve real-world problems on web-scale data
- Strong interest and experience in recommendation systems and causal inference
- Proficiency in quantitative programming languages such as Python or R
- Strong data manipulation skills using SQL and Spark
Responsibilities
- Develop methods and models to explain why certain content is being promoted or not for a Pinner
- Work on pioneering zero-to-one product initiatives
- Develop a deep understanding of Pinterest Trends, Pinner Needs, and our recommendation system
- Generate insights and robust methodologies to answer the fundamental “why” behind content promotion decisions
- Build a comprehensive understanding of Pinterest’s Curation ecosystem, including opportunities and Pinner needs related to boards, trends, collages, and more
- Develop and implement robust frameworks combining online and offline methods to analyze recommendation outputs
- Apply scientific rigor and advanced statistical techniques to address challenges in product creation, development, and enhancement, with a focus on user behavior
Other
- 7+ years of experience analyzing data in fast-paced, data-driven environments
- Ability to independently manage and drive projects to completion
- Excellent written and verbal communication skills, capable of explaining complex insights to both technical and non-technical audiences
- Collaborative mindset with a proven track record of partnering effectively with cross-functional teams
- Bachelor’s or Master’s degree in computer science, statistics, data science, or a related field, or equivalent professional experience
