Member of Technical Staff (Data Scientist)

About the role

This role accelerates the AI-native data workflow, building AI agents that conduct full analyses autonomously and making the entire data warehouse AI-readable. The team is already working this way, and you'll take what's working and turn it into repeatable systems, scalable tools, and patterns that the data team and the entire company can adopt.

Responsibilities

• Accelerate the AI-native data workflow
• Build AI agents that do data science, not just answer SQL questions, but conduct end-to-end analyses: explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations
• Make the warehouse AI-readable - build the semantic layer, context, and retrieval infrastructure that lets any AI system (internal or product) query Perplexity's data accurately and reliably
• Automate the data lifecycle - self-healing pipelines, automated dbt model generation and validation, data quality agents that detect, diagnose, and fix issues autonomously
• Ship AI-powered experiment analysis - agents that interpret A/B test results, flag statistical issues, and draft ship/no-ship recommendations for product teams
• Own the full lifecycle - from identifying the highest-leverage problem, to prototyping with LLMs, to iterating on accuracy and UX, to production deployment and monitoring
• Turn the data team into a product team - build internal data products that stakeholders across the company actually use daily, replacing ad-hoc requests with self-serve AI interfaces

Requirements

6-8+ years in data science, analytics engineering, or a related role - you've been in the data trenches

Strong product sense - you've worked closely with product and business teams, you understand what drives user behavior, and you have good instincts for what to measure and what to build

Deep SQL expertise - you think in SQL, you've built data models, you know your way around a warehouse

Pipeline experience - you've built and maintained data pipelines, worked with dbt, dealt with data quality issues firsthand

Enough software engineering chops to be dangerous - you can build and ship a working tool in Python, not just a notebook. You can wrangle APIs, deploy a service, write code that other people can maintain. You're not a SWE, but you're not afraid of production

Genuinely excited about AI - you've been building with LLMs on your own time. You have opinions about which models are good at what. You've tried building agents, RAG systems, or AI-powered workflows. You follow the space obsessively because you think it's going to change everything - including how data teams work

Builder mentality - you see a manual process and you can't help but automate it. You ship fast and iterate

Autonomy - this is a new function. You'll define the roadmap as much as execute it

Qualifications

Bonus Experience:

• With dbt (building and maintaining production models)
• Snowflake administration and optimization
• You've built Slack bots, internal CLI tools, or developer productivity tools that people actually used
• Experience with AI agent frameworks
• Experience with BI tools - you know what's worth automating because you've done the manual version
• You've designed experiments and analyzed results in A/B testing and experimentation

Skills

None specifically listed, but strong technical skills in data science, analytics engineering, and software engineering are required.

Benefits

Not specified in the job posting.

Pay

$175K - $330K

Schedule

Not specified in the job posting.