Staff Data Scientist, Engagement Ecosystem

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🕒 April 1

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Zigsaw

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

Making Job-search and talent discovery simpler, faster and effective for Job-seekers.

📋 Description

• 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. • Deep dives on how engagement metrics impact monetization and retention. • Understanding and predicting the value of core behaviors (e.g., saving, repinning, board creation) as they relate to downstream business outcomes. • 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. • Use advanced ML, causal inference, and generative AI techniques to model and explain complex interactions across the Pinterest ecosystem (e.g., organic engagement, ads, content quality, monetization), turning highly ambiguous business questions into clear, testable hypotheses and decision frameworks for Core and Monetization leadership. • Apply AI-assisted analysis and developer tools (e.g., copilot, intelligent dashboards) to accelerate exploration, improve code quality, and scale insight generation, ensuring we can quickly evaluate tradeoffs like ad load vs. Pinner engagement. • Champion responsible and scientifically rigorous use of AI across the ecosystem, setting best practices for instrumentation, experimentation, and metric design so that AI-driven changes balance engagement, revenue, and long-term user trust.

🎯 Requirements

• 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. • Proven track record applying machine learning and/or causal inference to complex, ambiguous product or marketplace problems at scale (e.g., ads, recommendations, multi-sided ecosystems), from problem framing and experimental design through to influencing senior stakeholders and driving product or policy decisions. • Hands-on experience with modern AI/ML tools and workflows (such as PyTorch, TensorFlow, scikit-learn, or similar, plus LLM-based assistants or copilots) and comfort rapidly adopting new AI capabilities into day-to-day analysis, prototyping, and experimentation workflows. • Masters degree in a technical field (e.g., Computer Science, Statistics, Mathematics, Engineering, Social Sciences).

🏖️ Benefits

• Information regarding the culture at Pinterest and benefits available for this position can be found here.