perplexity
Perplexity is AI for people who expect more. This role brings that standard to how we understand our users, shape our product, and decide what to build next.
We're looking for a data scientist who can turn product questions into clear decisions. You'll work across product, engineering, growth, design, and user research to understand user behavior, define metrics, design experiments, and identify the highest-leverage opportunities for the company.
The data team is AI-native by default. We use AI to accelerate analysis, write and review code, explore hypotheses, document data, generate first drafts, and automate recurring workflows. But the core of the role is still judgment: asking the right question, choosing the right metric, designing the right test, and knowing when the answer is strong enough to act on.
Develop product insights - analyze user behavior to inform product roadmap, accelerate adoption, and identify opportunities to improve the user experience.
Design and analyze experiments - form hypotheses, define success metrics, run A/B tests, interpret results, and turn findings into product recommendations.
Define the metrics that matter - build the metrics, guardrails, dashboards, and reporting workflows that help teams understand product and company health.
Partner across functions - work closely with engineering, product, growth, design, and user research to answer ambiguous questions and drive decisions.
Build reusable data assets - create tables, models, and documentation that make analysis faster, more consistent, and easier for humans and AI systems to use.
Use AI to scale data science - automate recurring analysis, build AI-assisted workflows, improve documentation, and turn one-off investigations into repeatable systems.
Tell the story clearly - communicate findings, assumptions, uncertainty, and recommendations in a way that helps teams make decisions.
6+ years of experience as a data scientist or closely related role.
Strong product sense - you understand user behavior, product tradeoffs, and how to connect analysis to decisions.
SQL expertise - you can navigate a complex data warehouse on your own, reason about grain and joins, and debug data issues when something looks wrong.
Experimentation depth - you have significant experience designing, running, and analyzing A/B tests.
AI-native working style - you use LLMs and AI tools to move faster without outsourcing analytical judgment.
Metrics and dashboard experience - you've built useful reporting in tools such as Omni, Mode, Hex, Looker, or similar, and know how to turn metrics into better product decisions.
Comfort with ambiguity - you can take an open-ended question, structure it, execute the analysis, and make a clear recommendation.
End-to-end ownership - you take responsibility for the work from problem definition to stakeholder adoption.
Experience with dbt, data modeling, or analytics engineering.
Python experience for analysis, automation, or internal tools.
Experience with Snowflake, especially performance and cost-aware querying.
Experience combining qualitative user research with quantitative product analysis.
Experience as one of the first data scientists at an early or growth-stage company.
ML experience or experience working across multiple product surfaces.
Define data science at Perplexity - you'll help set the bar for how an AI-native data science team operates.
Work on questions with no playbook - AI-native products are too new for established benchmarks. What engagement, retention, and user success look like for AI products that answer questions and get real work done is still an open question, and you'll help define it.
Use AI where it actually matters - you'll use frontier tools to make analysis faster, more repeatable, and more useful.
Direct impact - small team, high ownership, and work that shapes product and company direction.
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