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Role Description

We're looking for a sharp, stats-literate Analyst who can own product analytics end-to-end. We need someone with the judgment to know what questions to ask and the rigor to answer them properly.

• Own experimentation.
• Design A/B tests with proper sample size calculations, power analysis, and significance testing. Run them. Interpret them. Flag when results are misleading.
• Analyze retention and engagement.
• Build and maintain cohort analyses, retention curves, and conversion funnels. Identify what separates users who stick from users who churn.
• Answer the hard questions.
• Define and track metrics.
• Help us build the right metrics framework for our stage. Know when a metric is vanity and when it's signal.
• Communicate findings clearly.
• Present insights to technical and non-technical stakeholders in a way that drives action, not confusion.
• Use LLMs as a force multiplier.
• We expect you to use AI tools aggressively for query generation, data wrangling, and visualization -- so you can spend your time on the thinking, not the typing.

Qualifications

• 3-5 years of experience in product analytics, data analysis, or a quantitative role at a tech company (startup experience strongly preferred)
• Strong statistical foundations: hypothesis testing, confidence intervals, Bayesian reasoning, power analysis, regression. Not textbook knowledge -- practical application.
• Demonstrated ability to design and analyze A/B tests and other controlled experiments
• Sharp product intuition -- you think about why users behave a certain way, not just how
• Excellent written and verbal communication

Requirements

• Fluent in SQL. You'll be writing HogQL (ClickHouse-flavored SQL) against PostHog, so comfort with analytical SQL dialects is important.
• Python proficiency (pandas, scipy, statsmodels) for ad hoc analysis beyond what a BI tool can do
• Experience with PostHog or similar product analytics platforms (Amplitude, Mixpanel)
• Experience at an early-stage startup where you had to build analytics from scratch

What we don't need

• ML/data science specialization (we're not building recommender systems)
• Data engineering / pipeline skills (this isn't a dbt or Airflow role)
• A Master's degree (we care about what you can do, not your credentials)

Interview Process

• Intro call (30 min) -- get to know each other, talk through your experience
• Stats & analysis discussion (~1.5 hrs) -- assess your product analytics skills and stats fluency
• Culture & product chat with CEO (30 min) -- alignment on mission, working style, product thinking

Details

• Location: Remote (some overlap with US PST hours expected)
• Compensation: commensurate with experience. Equity included.
• Team: You'll be joining a small, high-caliber team across the world. Direct line to founders and engineering leadership.