Staff Data Scientist, Personalization & Shopping
Staff Data Scientist, Personalization & Shopping
Job Description
Responsibilities
- 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.
Qualifications
- 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.
- Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.
Locations
- New York, NY, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 280,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learningintermediate
- Causal inferenceintermediate
- Recommendation systemsintermediate
- Quantitative programming (Python/R)intermediate
- Data manipulation (SQL/Spark)intermediate
- Scientific methods on web-scale dataintermediate
- Statistical methodsintermediate
- Cross-functional collaborationintermediate
- Communication skillsintermediate
Required Qualifications
- 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. (experience)
- Strong interest and experience in recommendation systems and causal inference. (experience)
- Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark). (experience)
- Ability to work independently and drive your own projects. (experience)
- Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners. (experience)
- A team player eager to partner with cross-functional partners to quickly turn insights into actions. (experience)
- Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience. (experience)
Responsibilities
- 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.
Tags & Categories
Staff Data Scientist, Personalization & Shopping
Staff Data Scientist, Personalization & Shopping
Job Description
Responsibilities
- 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.
Qualifications
- 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.
- Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.
Locations
- New York, NY, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 280,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learningintermediate
- Causal inferenceintermediate
- Recommendation systemsintermediate
- Quantitative programming (Python/R)intermediate
- Data manipulation (SQL/Spark)intermediate
- Scientific methods on web-scale dataintermediate
- Statistical methodsintermediate
- Cross-functional collaborationintermediate
- Communication skillsintermediate
Required Qualifications
- 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. (experience)
- Strong interest and experience in recommendation systems and causal inference. (experience)
- Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark). (experience)
- Ability to work independently and drive your own projects. (experience)
- Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners. (experience)
- A team player eager to partner with cross-functional partners to quickly turn insights into actions. (experience)
- Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience. (experience)
Responsibilities
- 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.
Tags & Categories