GTM Engineer

Harper is an AI-native commercial insurance company in San Francisco. We're not bolting AI onto insurance — we're rebuilding the entire business as software, on a simple bet: turning expert human judgment into compute is one of the largest transitions left to make, and a trillion-dollar industry still run 90% by hand is the place to prove it. We've grown ~100x in the last year and we move at that speed — on-site, in person, long days, very high standards. Almost no one joins Harper for insurance; they join to build the company that replaces how it works.

Growing 100x in a year is a data problem before it's a marketing problem. To keep that pace honest, someone has to know — with precision — what's working, what isn't, and why, and turn that into where the next dollar goes. This is a founding role that owns the insight layer behind growth from day one: every model you build, every experiment you run, every cohort you cut becomes the company's permanent understanding of how it acquires customers. You sit at the seam of marketing, analytics, and product and work directly with the Head of Marketing and the founders. No committee, no approval chain — you surface it, you influence it, you own the outcome. Winning in 6–12 months means measurable revenue impact and a growth-analytics engine that runs without you holding it up.

• Own the funnel. Track performance from lead to conversion to revenue; define and maintain the core marketing KPIs the company runs on.

• Direct paid acquisition. Analyze channel performance across Google, Meta, and beyond, and influence where the budget actually goes.

• Model the unit economics. Build and interpret LTV/CAC so the business knows which levers are real and which are noise.

• Run experiments that decide things. Design and analyze A/B tests, then turn the result into a decision — not a slide.

• Find what's working. Cohort analysis, segmentation, channel attribution — isolate the signal and double down.

• Move people with the analysis. Translate complex work into clear recommendations a non-technical room acts on.

• A proven growth, marketing, or revenue-focused data scientist — you've worked with growth teams, not supported them from a distance.

• Strong SQL; solid Python.

• Hands-on with funnel analysis, attribution, segmentation, and LTV/CAC modeling, plus designing and reading A/B tests.

• Exposure to paid channels (Google, Meta, TikTok) and how attribution actually behaves.

• A commercial instinct: you tie data to revenue and influence decisions, not just report metrics. You think in first principles and move fast when the ground is ambiguous.

• Bonus: PostHog/Mixpanel/Amplitude; PLG or SMB-heavy environments; AI-assisted analytics workflows.

This is not a dashboarding, BI, or ML-research role. The work is commercial: change what the business does, then prove it changed the number.

On-site in San Francisco, in person, long days, high standards. As a founding analyst you're close to the founders and to the decisions — the upside is real influence from week one, the cost is the pace that comes with 100x growth and ~1,000 new customers a month. The right person reads that as the reason to come.

• Compensation: $130,000–$190,000 base + performance bonus + equity.

• Location: San Francisco, in-office. Based here or willing to relocate.

• Benefits: Uber commuter benefits; breakfast, lunch, and dinner provided; snacks and coffee stocked; free gym membership; health, dental, and vision.

• Process: People screen → lead screen → Super Day.

To apply: Data talks, narratives walk. Send your resume and one example of analysis that drove a real business decision.