About this role
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.
ResponsibilitiesDiscover 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
QualificationsMS 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
Preferred QualificationsProficiency 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