Sr. Data Scientist, AI/ML Systems

Pinterest•San Francisco, CA

1d•Remote

About The Position

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. As we scale and unify our foundational AI models across every recommendation surface, rigorously measuring the system-level impact of these advances is essential to our ability to ship with confidence and compound gains over time. We are looking for a Senior Data Scientist to serve as the measurement backbone for Pinterest's foundational model initiatives within the Advanced Technology Group (ATG). In this role, you will design the evaluation frameworks, experimentation strategies, and system-level metrics that quantify how improvements to our largest and most complex models translate into real user and business outcomes. You will work in a highly collaborative and cross-functional environment, partnering with ML Engineers, Applied Scientists, Product Managers, and ML Platform engineers. You are expected to develop a deep understanding of Pinterest's recommendation ecosystem, from representation learning to retrieval, ranking, reinforcement learning, and beyond, and bring the analytical rigor necessary to decompose, attribute, and communicate the impact of foundational model changes across this interconnected system. The results of your work will directly influence model investment decisions, launch criteria, and the pace of innovation across ATG and Pinterest.

Requirements

5+ 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 hands-on experience in one or more of: ML system evaluation, recommender system measurement, A/B experimentation at scale, causal inference
Deep familiarity with large-scale recommendation or ranking systems and their evaluation including an understanding of how representation learning, retrieval, ranking, and re-ranking stages interact and compound in production.
Experience designing and executing A/B experiments for complex ML systems, including multi-surface holdouts, metric decomposition, long-run effect estimation, and interference/spillover mitigation.
Strong quantitative programming (Python) and data manipulation skills (SQL/Spark); experience with ML pipelines, feature stores, and large-scale experimentation platforms.
Ability to work independently, drive ambiguous projects end-to-end, and operate with high ownership in a fast-moving research-to-production environment.
Excellent written and verbal communication skills, with the ability to translate complex system-level findings into clear narratives for technical and non-technical partners including leadership-level investment recommendations.
A team player eager to partner across teams to turn measurement insights into better models and faster launches.

Responsibilities

Design and execute system-level measurement frameworks for foundational model improvements spanning offline evaluation benchmarks, online A/B experiments, and longitudinal impact tracking across surfaces.
Define, and own the success metrics that quantify foundational model value.
Build causal inference methodologies to isolate the incremental impact of individual model components within a complex, multi-model production system where changes co-occur and interact.
Work cross-functionally to build relationships, proactively communicate key findings, and collaborate closely with ML Engineers, Applied Scientists, Homefeed and Surface teams to ensure measurement rigor is embedded in every model launch.
Relentlessly focus on impact, whether through sharpening investment decisions with data, raising the bar for launch criteria, accelerating experimentation velocity, or surfacing hidden inefficiencies in the model ecosystem.