About the job
At Perplexity, we believe in harnessing the power of AI to exceed expectations. This role is pivotal in ensuring our data team embodies that philosophy, placing AI at the core of our operations.
We seek an exceptional individual with a strong background as a data scientist, analytics engineer, or data engineer. You are the type of professional who understands which metrics hold true significance, can design insightful A/B tests, and delve into data models to uncover discrepancies. Importantly, you recognize that your highest impact opportunity lies in creating AI systems that revolutionize the practice of data science.
This is not about developing another text-to-SQL bot or merely producing another dashboard. Instead, you'll create AI agents capable of performing comprehensive analyses autonomously – generating hypotheses, executing queries, interpreting outcomes, and crafting actionable recommendations. Your work will ensure that our entire data warehouse is AI-accessible, enabling any system to query it with precision. You'll engineer self-correcting pipelines that identify and resolve data issues proactively. Your contributions will empower a lean data team to operate with the efficiency of a much larger organization.
Join a forward-thinking data team that already integrates AI into its processes, yet recognizes the vast potential that lies ahead. With full support from leadership, we are committed to transforming our initial efforts into a world-class framework: scalable systems, innovative tools, and an AI-first approach that not only elevates our status but also propels the industry forward.
What You'll Do
Enhance the AI-driven data workflow - Building on existing successes, you will establish repeatable systems and scalable tools that the data team and the broader organization can adopt.
Create AI agents for data science - Your focus will be on developing agents that not only handle SQL inquiries but also manage end-to-end analyses: exploring data, formulating hypotheses, executing queries, interpreting results, and delivering actionable insights.
Make the data warehouse AI-friendly - Design the semantic layers, context, and retrieval systems that enable any AI platform (internal or external) to query Perplexity's data effectively and reliably.
Automate the data lifecycle - Implement self-healing pipelines, automated dbt model creation and validation, and data quality agents that autonomously detect and resolve issues.
Deliver AI-enhanced experiment analysis - Develop agents capable of interpreting A/B test results, identifying statistical anomalies, and providing thoughtful insights.
