Member of Data Staff (Data Acceleration)

• 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.

Why This Role

• 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.

What We're Looking For

• 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.

Bonus

• 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.