Staff Data Scientist, Ads Delivery @Pinterest

Overview

Pinterest is seeking a Staff Data Scientist for Ads Delivery, tasked with shaping the future of both people-facing and business-facing products. This role will utilize expertise in quantitative modeling, experimentation, and algorithms to tackle complex engineering challenges while collaborating with cross-functional partners.

Key Responsibilities

• Develop a nuanced understanding of Pinterest ads delivery, quantifying opportunities and risks.
• Lead projects on Ads Delivery opportunities across different funnel stages.
• Design and productionize robust, scalable machine learning and evaluation frameworks encompassing forecasting, recommendation, and causal inference.
• Advocate for best-in-class experimentation, instrumentation, and metric design to bridge short-term proxy metrics with long-term business impact.
• Translate complex data questions into actionable business insights by collaborating across disciplines.
• Mentor and guide junior and senior scientists, fostering intellectual curiosity and driving technical excellence.

Requirements

• 10+ years of experience in web-scale data environments with a track record in product, engagement, or ecosystem analytics.
• Expertise in Machine Learning, Statistical Modeling, Causal Inference, and Product analytics/strategy.
• Proficiency in Python/R and advanced SQL/Spark.
• Strong product intuition for designing solutions for high-impact business problems.
• Exceptional communication skills with the ability to simplify complex concepts for diverse audiences.
• Experience mentoring data talent at the staff/senior IC level.
• Cross-functional leadership capabilities aligned towards shared goals.
• Bachelor’s/Master’s degree in Computer Science or a relevant field, or equivalent experience.

Benefits

The salary for this position ranges from $164,695 to $339,078 USD, and the role is eligible for equity as well.

Location

This role is remote but requires commuting to the office for in-person collaboration 1-2 times per week, with proximity to offices in San Francisco, Palo Alto, or Seattle.

How to Apply

For more information and to submit an application, please visit the provided link.