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Perplexity is trying to automate the analysis workflow itself, building AI agents that form hypotheses, write queries, and draft recommendations so a small data team can do the work of a much larger one, which signals an AI-native internal operating model rather than just AI products for customers.
Perplexity is AI for people who expect more. This role brings that same standard to how our data team works, with AI at the center of everything we do.
We're looking for someone who's been a great data scientist, analytics engineer, or data engineer: the kind of person who knows which metric actually matters, can design an A/B test that answers the real question, has gone deep on a data model because something didn't add up, and has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done.
You'll build AI agents and internal systems that can increasingly handle end-to-end analysis workflows: forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with the right evaluation, review, and guardrails. You'll build the retrieval infrastructure and evaluation loops that let AI systems query the warehouse reliably. You'll create workflows that detect, diagnose, and help fix data issues before they become company-wide problems. You'll build the infrastructure that multiplies what a small data team can ship.
You'll join a data team that's already using AI across its work. The next step is turning those individual workflows into scalable systems, shared tools, and an AI-native operating model that becomes a benchmark for how modern data teams work.
Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops.
Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately.
Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt.
Automate the data lifecycle - build self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting.
Ship AI-powered experiment analysis - build agents that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations.
Turn the data team into a product team - build internal data products that stakeholders use every day, replacing ad hoc requests with self-serve AI interfaces.
Own the full lifecycle - identify high-leverage problems, prototype with LLMs, evaluate accuracy, design the UX, ship to production, and monitor quality over time.
6+ years in data science, analytics engineering, data engineering, or a related role. You've been close enough to real data work to know what should and should not be automated.
Deep SQL and analytics judgment - you can reason through metrics, experiments, data models, and messy warehouse reality without relying on a tool to think for you.
Strong product sense - you understand what stakeholders actually need, what makes a workflow adoptable, and how to turn a prototype into a product people use.
Hands-on LLM experience - you've built with frontier models, agents, RAG systems, evals, or AI-powered workflows and have opinions about where they work and where they fail.
Pipeline and modeling fluency - you've worked with dbt, warehouse schemas, data quality issues, and the practical tradeoffs behind durable data systems.
Builder mentality - you see a manual process and immediately think about how to systematize it. You ship fast, measure quality, and iterate.
Autonomy - this is a new function. You'll help define the roadmap as much as execute it.
Experience building production AI agents or agent evaluation systems.
Experience with Snowflake, semantic layers, or metadata systems.
Experience building internal tools, Slack bots, CLIs, or developer productivity products that people actually used.
Strong experimentation background, including metric design and statistical interpretation.
Experience with BI tools and the judgment to know what should be automated versus kept human-reviewed.
Early-stage startup experience.
Set the standard for the industry - Perplexity's data team is already using AI across its work. You'll turn that into something other data teams look to as the benchmark.
Build AI with AI - Perplexity builds AI for people who expect more. You'll bring that same level of ambition to how the company works with data.
Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible.
Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data.
Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters.
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