Staff Data Scientist, Engagement Ecosystem
Staff Data Scientist, Engagement Ecosystem
Job Description
Responsibilities
- Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
- Lead projects on tradeoffs between organic engagement and advertising.
- Lead projects on deep dives on how engagement metrics impact monetization and retention.
- Lead projects on understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
- Lead projects on designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
- Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
- Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
Qualifications
- 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics.
- Deep expertise in Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark.
- Strong product intuition—ability to scope, question, and design the right solutions for ill-defined, high-impact business problems.
- Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes.
- Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences.
- Track record mentoring and growing data talent at the staff/senior IC level.
- Cross-functional leadership and the ability to align competing interests towards shared goals.
- Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences).
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 250,000 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 Learning (recommendation, ranking, prediction, experimentation)intermediate
- Statistical Modeling & Causal Inference (observational and experimental data)intermediate
- Product analytics/strategy (root cause, goaling, design collaboration)intermediate
- Programming in Python/Rintermediate
- Advanced SQL/Sparkintermediate
Required Qualifications
- 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics. (experience)
- Deep expertise in Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark. (experience)
- Strong product intuition—ability to scope, question, and design the right solutions for ill-defined, high-impact business problems. (experience)
- Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes. (experience)
- Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences. (experience)
- Track record mentoring and growing data talent at the staff/senior IC level. (experience)
- Cross-functional leadership and the ability to align competing interests towards shared goals. (experience)
- Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences). (experience)
Responsibilities
- Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
- Lead projects on tradeoffs between organic engagement and advertising.
- Lead projects on deep dives on how engagement metrics impact monetization and retention.
- Lead projects on understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
- Lead projects on designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
- Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
- Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
Tags & Categories
Staff Data Scientist, Engagement Ecosystem
Staff Data Scientist, Engagement Ecosystem
Job Description
Responsibilities
- Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
- Lead projects on tradeoffs between organic engagement and advertising.
- Lead projects on deep dives on how engagement metrics impact monetization and retention.
- Lead projects on understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
- Lead projects on designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
- Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
- Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
Qualifications
- 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics.
- Deep expertise in Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark.
- Strong product intuition—ability to scope, question, and design the right solutions for ill-defined, high-impact business problems.
- Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes.
- Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences.
- Track record mentoring and growing data talent at the staff/senior IC level.
- Cross-functional leadership and the ability to align competing interests towards shared goals.
- Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences).
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
164,695 - 339,078 USD / yearly
Estimated Salary Rangemedium confidence
180,000 - 250,000 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 Learning (recommendation, ranking, prediction, experimentation)intermediate
- Statistical Modeling & Causal Inference (observational and experimental data)intermediate
- Product analytics/strategy (root cause, goaling, design collaboration)intermediate
- Programming in Python/Rintermediate
- Advanced SQL/Sparkintermediate
Required Qualifications
- 10+ years of hands-on experience in web-scale data environments, with a track record of solving hard, ambiguous problems in product, engagement, or ecosystem analytics. (experience)
- Deep expertise in Machine Learning (recommendation, ranking, prediction, experimentation), Statistical Modeling & Causal Inference (observational and experimental data), Product analytics/strategy (beyond dashboards: root cause, goaling, design collaboration), Programming in Python/R and advanced SQL/Spark. (experience)
- Strong product intuition—ability to scope, question, and design the right solutions for ill-defined, high-impact business problems. (experience)
- Scientific rigor and healthy skepticism: You challenge assumptions, find flaws, and drive towards robust, reproducible outcomes. (experience)
- Exceptional communication: You make the complex simple, and can influence both technical and non-technical audiences. (experience)
- Track record mentoring and growing data talent at the staff/senior IC level. (experience)
- Cross-functional leadership and the ability to align competing interests towards shared goals. (experience)
- Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences). (experience)
Responsibilities
- Develop a deep, nuanced understanding of the Pinterest engagement ecosystem and key product surfaces, quantifying ecosystem-level opportunities and risks.
- Lead projects on tradeoffs between organic engagement and advertising.
- Lead projects on deep dives on how engagement metrics impact monetization and retention.
- Lead projects on understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes.
- Lead projects on designing and evaluating interventions that sustainably boost enterprise metrics across product boundaries.
- Design and productionize robust, scalable ML and evaluation frameworks—spanning forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design; bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate across disciplines—Product, Engineering, Research, Business, and Design—translating complex data questions into actionable business insights.
- Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.
Tags & Categories