Data Scientist (Member of Data Staff)
Posted Updated
- We’re looking for a data scientist who can turn product questions into clear decisions. You’ll work across product, engineering, growth, design, and user research to understand user behavior, define metrics, design experiments, and identify the highest-leverage opportunities for the company
- The data team is AI-native by default. We use AI to accelerate analysis, write and review code, explore hypotheses, document data, generate first drafts, and automate recurring workflows. But the core of the role is still judgment: asking the right question, choosing the right metric, designing the right test, and knowing when the answer is strong enough to act on
- Develop product insights - analyze user behavior to inform product roadmap, accelerate adoption, and identify opportunities to improve the user experience
- Design and analyze experiments - form hypotheses, define success metrics, run A/B tests, interpret results, and turn findings into product recommendations
- Define the metrics that matter - build the metrics, guardrails, dashboards, and reporting workflows that help teams understand product and company health
- Partner across functions - work closely with engineering, product, growth, design, and user research to answer ambiguous questions and drive decisions
- Build reusable data assets - create tables, models, and documentation that make analysis faster, more consistent, and easier for humans and AI systems to use
- Use AI to scale data science - automate recurring analysis, build AI-assisted workflows, improve documentation, and turn one-off investigations into repeatable systems
- Tell the story clearly - communicate findings, assumptions, uncertainty, and recommendations in a way that helps teams make decisions
- Strong product sense - you understand user behavior, product tradeoffs, and how to connect analysis to decisions
- 6+ years of experience as a data scientist or closely related role
- Metrics and dashboard experience - you’ve built useful reporting in tools such as Omni, Mode, Hex, Looker, or similar, and know how to turn metrics into better product decisions
- End-to-end ownership - you take responsibility for the work from problem definition to stakeholder adoption
- SQL expertise - you can navigate a complex data warehouse on your own, reason about grain and joins, and debug data issues when something looks wrong
- Experimentation depth - you have significant experience designing, running, and analyzing A/B tests
- AI-native working style - you use LLMs and AI tools to move faster without outsourcing analytical judgment
- Comfort with ambiguity - you can take an open-ended question, structure it, execute the analysis, and make a clear recommendation
- Experience with dbt, data modeling, or analytics engineering
- Python experience for analysis, automation, or internal tools
- Experience combining qualitative user research with quantitative product analysis
- Experience with Snowflake, especially performance and cost-aware querying
- Experience as one of the first data scientists at an early or growth-stage company
- ML experience or experience working across multiple product surfaces