Member of Data Staff (Data Acceleration)

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. You'll build AI agents and internal systems that can increasingly handle end-to-end analysis workflows, build the retrieval infrastructure and evaluation loops that let AI systems query the warehouse reliably, create workflows that detect, diagnose, and help fix data issues, and 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.

Responsibilities

Build AI agents that do data science, including systems that explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with evaluation and human review loops; Make AI systems query the warehouse reliably by building retrieval infrastructure and evaluation loops; Accelerate the AI-native data workflow by turning AI-assisted workflows into repeatable systems, reusable tools, and team-wide patterns; Automate the data lifecycle with self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows; Ship AI-powered experiment analysis, including 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 by building internal data products and self-serve AI interfaces; Own the full lifecycle from identifying problems and prototyping with LLMs to evaluating accuracy, designing UX, shipping to production, and monitoring quality

Requirements

6+ years in data science, analytics engineering, data engineering, or a related role; Deep SQL and analytics judgment, including reasoning through metrics, experiments, data models, and messy warehouse data; Strong product sense, including understanding stakeholder needs and turning prototypes into products people use; Hands-on LLM experience with frontier models, agents, RAG systems, evals, or AI-powered workflows; Pipeline and modeling fluency with dbt, warehouse schemas, data quality issues, and durable data systems; Builder mentality with a focus on systematizing manual processes, shipping fast, measuring quality, and iterating; Autonomy to help define the roadmap as well as execute it

Nice to have

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

Benefits

Set the standard for the industry; Build AI with AI; Frontier models, day one; Massive leverage; Direct impact, with a small team and no layers of approval