Member Of Technical Staff (Data Scientist)
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.
What this role actually needs.
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. Responsibilities: - Accelerate the AI-native data workflow - the team is already working this way. 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 - 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 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 Company context: Perplexity is an answer engine — an AI-native search product used by millions of professionals daily.
Day-to-day expectations
Perplexity lists these responsibilities for the Member Of Technical Staff (Data Scientist) role.
- Accelerate the AI-native data workflow - the team is already working this way. 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
- 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
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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
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Source: jobs.ashbyhq.com · Source ID: d680e788-14d3-43f6-8ce8-5df486ca32d0 · Confidence: 94/100 · Last checked: May 7, 2026
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