Founding Data Scientist

FLORA•New York, NY

4d•Onsite

About The Position

About FLORA We build creative tools for the new creative class—an intelligent canvas where the best AI models enable professional craft. We're a team of ~20 who recently closed our Series A, backed by investors like Hanabi Capital, Menlo Ventures, Justin Kan, and Gabe Whaley (founder of MSCHF). Our customers include Pentagram, Lionsgate, and Nike. We're looking for a Founding Data Scientist who will build FLORA's data function from the ground up. As part of the founding team, you will work closely with the CTO and Head of Product to define what data means at this company—how we capture it, store it, analyze it, and turn it into decisions. You'll stand up our warehousing and pipelines, define the metrics that matter (activation moments, power users, generation quality), and build dashboards and analysis workflows. You'll also work with our Intelligence team (led by a veteran from Scale AI) on the data layer around AI generations. We integrate 200+ models and believe deeply understanding generation patterns will establish FLORA as the creative industry's authority on AI. We're looking for people who have

Requirements

Proven ability to work autonomously—you'll be the first data hire and we're counting on your expertise
Deep SQL fluency and strong fundamentals in at least one modern data warehouse (Snowflake, BigQuery, Databricks, ClickHouse, etc.)
Experience building data infrastructure, not just querying it—you've set up pipelines, defined schemas, made tooling decisions
Strong product instincts—you know what to measure, what's vanity, and how to frame findings for stakeholders
Comfort operating in hypergrowth—you can build fast and while also building for scale
The ambition to see this as a path to leading a data team

Nice To Haves

TypeScript chops—we're full-stack TS and you'll collaborate closely with engineers
Sophisticated PostHog experience (cohorts, funnels, feature flags, warehouse sync)
Experience with creative tools or AI-native products
Familiarity with unstructured data—LLM outputs, prompt patterns, generation analytics
A background building evaluation frameworks for AI/ML systems

Responsibilities

Work closely with the CTO and Head of Product to define what data means at this company—how we capture it, store it, analyze it, and turn it into decisions.
Stand up our warehousing and pipelines
Define the metrics that matter (activation moments, power users, generation quality)
Build dashboards and analysis workflows.
Work with our Intelligence team (led by a veteran from Scale AI) on the data layer around AI generations.