Product Analyst (Remote)

About Littlebird

The Role

What You'll Do

• 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. "Does Meeting Notes drive paid conversion, or do power users just happen to use it?" -- that kind of thing. Correlation vs. causation is your bread and butter.
• 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.

What We're Looking For

• 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

• 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

• 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

1. Intro call (30 min) -- get to know each other, talk through your experience
2. Stats & analysis discussion ( ~1.5 hrs) -- assess your product analytics skills and stats fluency
3. 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.
  • We love to hear when birds chirp!