Data Scientist​/Engineer – Online Metrics

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search, and specialized datasets, combined with both frontier and in-house models, create the best possible experience for our users. As a Data Scientist/Engineer on this team, you will derive online signals from user interactions to bridge the gap between changes in answer quality and observed user behavior.

• Discover and validate online signals from user interactions that serve as reliable proxies for true answer quality
• Design and implement novel online metrics to be tracked both in A/B testing and on product health dashboards, ensuring alignment with ground-truth evaluations
• Analyze experimental results to validate these metrics, ensuring they accurately predict user satisfaction and drive product decisions
• Build and maintain the data pipelines that calculate these metrics at scale, delivering actionable quality signals to Search, Product, and model training teams
• Communicate findings and bring clarity through close collaboration with Product and Search teams
• Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality

• MS in a technical field or equivalent experience
• 4+ years of experience working as a Data Scientist, Analytics Engineer, or related role
• Experience working on search, recommendation, or LLM-based products, with an emphasis on designing online metrics and analyzing A/B experiments
• Strong proficiency in Python and SQL (expected to write production-grade code)
• Deep knowledge of statistical analysis
• Experience with Business Intelligence (BI) tools for visualization and reporting
• Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster

• Proficiency with Apache Spark and Databricks
• Experience with the development or validation of LLM-as-a-judge systems
• Prior work supporting customer-facing products at scale