GTM Engineer
The Problem
36 million businesses in America need insurance—it’s not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing.
Over 90% of commercial insurance is still human-led. We’re building the inverse: 90%+ AI-led, pushing toward the higher 90s. Not by patching legacy workflows—by building AI that makes humans more effective, improves the customer experience, and eliminates friction at every step.
We’re adding ~1,000 customers per month. We’ve grown 100x since last year. We’re looking to do even more this year—and that’s why we’re hiring.
To grow that fast, we need to understand—with precision—what’s working, what’s not, and why.
The Thesis
This is a founding role. You'll own the insight layer behind growth from day one—shaping how marketing dollars are spent, how campaigns are optimized, and how we scale. Every model you build, every experiment you run, every insight you surface becomes company DNA. Winning in 6-12 months means measurable revenue impact AND a growth analytics engine that runs itself.
The Role
You're a commercial data scientist who sits at the intersection of marketing, analytics, and product—but make no mistake, this is a growth role, not a research role. You work closely with the Head of Marketing to answer the questions that matter: what's working, what's not, why, and what to do next.
You work directly with founders. No committee. No approval chain. You surface it, you influence it, you own the outcome.
What You’ll Do
Own funnel analytics — Track performance from lead to conversion to revenue; define and maintain core marketing KPIs
Drive paid acquisition decisions — Analyze channel performance across Google, Meta, and beyond; influence where the budget goes
Build and interpret LTV/CAC models — Give the business a clear picture of unit economics and what levers to pull
Run experiments that drive growth — Design and analyze A/B tests; turn results into decisions, not just reports
Segment and identify what's working — Cohort analysis, customer segmentation, high-performing channel identification
Communicate insights that move people — Translate complex analysis into clear, actionable recommendations for non-technical stakeholders
You Might Be a Fit If…
You've worked directly with marketing or growth teams—not just supported them from afar
You can tie data to revenue outcomes, not just report metrics
You've influenced budget allocation, campaign strategy, or targeting decisions
You have strong SQL and solid Python
You've analyzed paid acquisition channels and understand attribution
You think in first principles and move fast in high-ambiguity environments
You're based in San Francisco or willing to relocate
Requirements
Proven experience in a growth, marketing, or revenue-focused data science role
Strong SQL; Python preferred
Hands-on experience with funnel analysis, attribution, segmentation, and LTV/CAC modeling
Experience designing and analyzing A/B tests and experiments
Exposure to paid acquisition channels (Google, Meta, etc.)
Commercial mindset — comfort influencing decisions, not just informing them
This is not a dashboarding, BI, or ML research role
Nice to Have
Experience with Google Ads, Meta Ads, or TikTok Ads
Familiarity with PostHog, Mixpanel, or Amplitude
Background in PLG companies, high-growth startups, or SMB-heavy environments
Exposure to AI-assisted analytics workflows
Compensation
Salary: $130,000–$190,000 + performance bonuses & equity
Location: San Francisco, in-office
Benefits
Health, dental, and vision insurance
Commuter benefits
Team meals and snacks
The Process
People screen — Initial fit and alignment
Lead screen — Skills and culture fit
Super day — See how you operate in real time
To Apply
Data talks. Narratives walk. If you prove things instead of just believing them—send your resume and an example of analysis that drove a business decision.
