Staff Data Scientist - Personalization and Shopping
Pinterest is the world’s leading visual search and discovery platform, serving over 500 million monthly active users globally on their journey from inspiration to action. At Pinterest, Shopping is a strategic initiative that aims to help Pinners take action by surfacing the most relevant content, at the right time, in the best user-friendly way. We do this through a combination of innovative product interfaces, and sophisticated recommendation systems.
We are looking for a Staff Data Scientist with experience in machine learning and causal inference to help advance Shopping at Pinterest. In your role you will develop methods and models to explain why certain content is being promoted (or not) for a Pinner. You will work in a highly collaborative and cross-functional environment, and be responsible for partnering with Product Managers and Machine Learning Engineers. You are expected to develop a deep understanding of our recommendation system, and generate insights and robust methodologies to answer the “why”. The results of your work will influence our development teams, and drive product innovation.
What you’ll do:
Ensure that our recommendation systems produce trustworthy, high-quality outputs to maximize our Pinner’s shopping experience.
Develop robust frameworks, combining online and offline methods, to comprehensively understand the outputs of our recommendations.
Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners.
Work cross-functionally to build relationships, proactively communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest.
Relentlessly focus on impact, whether through influencing product strategy, advancing our north star metrics, or improving a critical process.
Mentor and up-level junior data scientists on the team.
What we’re looking for:
7+ 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.
Strong interest and experience in recommendation systems and causal inference.
Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark).
Ability to work independently and drive your own projects.
Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners.
A team player eager to partner with cross-functional partners to quickly turn insights into actions.
In-Office Requirement Statement:
We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-NM4
#LI-REMOTE
About the job
Apply for this position
Staff Data Scientist - Personalization and Shopping
Pinterest is the world’s leading visual search and discovery platform, serving over 500 million monthly active users globally on their journey from inspiration to action. At Pinterest, Shopping is a strategic initiative that aims to help Pinners take action by surfacing the most relevant content, at the right time, in the best user-friendly way. We do this through a combination of innovative product interfaces, and sophisticated recommendation systems.
We are looking for a Staff Data Scientist with experience in machine learning and causal inference to help advance Shopping at Pinterest. In your role you will develop methods and models to explain why certain content is being promoted (or not) for a Pinner. You will work in a highly collaborative and cross-functional environment, and be responsible for partnering with Product Managers and Machine Learning Engineers. You are expected to develop a deep understanding of our recommendation system, and generate insights and robust methodologies to answer the “why”. The results of your work will influence our development teams, and drive product innovation.
What you’ll do:
Ensure that our recommendation systems produce trustworthy, high-quality outputs to maximize our Pinner’s shopping experience.
Develop robust frameworks, combining online and offline methods, to comprehensively understand the outputs of our recommendations.
Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners.
Work cross-functionally to build relationships, proactively communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest.
Relentlessly focus on impact, whether through influencing product strategy, advancing our north star metrics, or improving a critical process.
Mentor and up-level junior data scientists on the team.
What we’re looking for:
7+ 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.
Strong interest and experience in recommendation systems and causal inference.
Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark).
Ability to work independently and drive your own projects.
Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners.
A team player eager to partner with cross-functional partners to quickly turn insights into actions.
In-Office Requirement Statement:
We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-NM4
#LI-REMOTE