Job description

Product Analyst (Remote) About Littlebird Littlebird is your AI-powered personal operating system. We capture context from your daily life -- meetings, messages, browsing, notes -- and use it to help you remember everything, prioritize your day, and solve real problems. Think of us as the AI assistant that actually knows you. Check out the recent TechCrunch news on our $11M seed funding and what users are saying about the product. We're growing fast, and we're entering a phase where gut instincts need to be backed by rigorous data. That's where you come in. The Role 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. Generative AI can write SQL. It can calculate p-values. What it can't do is design the right experiment, catch a subtle confound in your cohort analysis, or connect a qualitative user signal to a quantitative pattern. That's the job. You'll work closely with our C-suite and product leadership to drive decisions on retention, engagement, conversion, and experimentation. 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 Must-haves: • 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 Nice-to-haves: • 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 We keep it practical and respectful of your time: • 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. • We love to hear when birds chirp!

Requirements

Must-haves

Product Analyst (Remote)
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.
You'll work closely with our C-suite and product leadership to drive decisions on retention, engagement, conversion, and experimentation.
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.
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
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)

Nice-to-haves

3-5 years of experience in product analytics, data analysis, or a quantitative role at a tech company (startup experience strongly preferred)
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

Application prep

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