Member of Data Staff
Build AI agents and systems that automate end-to-end data science workflows including hypothesis formation, querying, analysis, and recommendations at Perplexity. Requires 6+ years in data roles, strong SQL/analytics judgment, production Python, hands-on LLM experience, and product sense to create scalable AI-native data infrastructure.
About the job
What You'll Do
- 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.
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.
- Production-oriented Python ability - you can build and ship working tools, wrangle APIs, evaluate model outputs, deploy services, and write code others can maintain.
- 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.
Skills
Python, SQL, LLMs, RAG, dbt, Snowflake, AI Agents, A/B Testing, Data Modeling, Semantic Layers
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