Data Scientist/Engineer – Online Metrics
Perplexity · New York City
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About the 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.
Responsibilities
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
Qualifications
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
Preferred Qualifications
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
In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is 2, and that of a fair die roll is 6; and generally, for a probability distribution with exactly N outcomes each having a probability of exactly 1 / N, the perplexity is simply N. But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.
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