Member of Data Staff (Data Acceleration)

San Franciscofull timeOn Site

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senior

$175k–$330k

H1B: yes

Perplexity Member of Data Staff at Perplexity building AI agents and internal systems to automate and scale data science workflows. This role involves designing AI-native data workflows, automating data lifecycle, and shipping AI-powered experiment analysis.

Responsibilities

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.

Qualification

6+ years in data scienceAutonomy - this is a new functionExperience with SnowflakeExperience building internal toolsStrong experimentation background

Required

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.