Description
We're looking for a senior, full-stack analyst / data scientist to own Product and PLG analytics end-to-end. You'll own the metrics behind our self-serve motion (signup, activation, usage, conversion, expansion and retention) and find the drivers behind them. You'll work directly with our CEO and leadership as a thought partner on product and growth decisions.
Responsibilities:
- Own Product and PLG analytics end-to-end: define, instrument and measure the self-serve funnel, from first API call through activation, conversion to paid, expansion and churn.
- Uncover the drivers behind top-level KPIs: which behaviors, use cases and product experiences predict activation, retention and monetization.
- Build what you need: data pipelines and models (SQL, dbt, Python, orchestration) that turn raw product, usage and billing data into trusted, reusable datasets.
- Build and ship predictive models (conversion propensity, churn risk, usage forecasting, lead and account scoring) and bring their outputs into product and GTM workflows.
- Design, run and evaluate experiments (A/B tests, pricing and packaging tests, onboarding changes) and apply causal inference where you can't run a clean experiment.
- Proactively monitor key business and product metrics, investigate trends and anomalies, and surface insights stakeholders can act on.
- Partner directly with the CEO and leadership on strategic questions, including pricing, growth levers, product bets and market opportunities, and turn ambiguous questions into clear analysis and decisions.
- Set the standard for how we measure product success: metric definitions, a shared source of truth and analytical rigor across teams.
Requirements:
- 5+ years of experience as a Product / Growth Analyst, Data Scientist or equivalent.
- 2+ years in a SaaS company, with deep understanding of funnels, cohorts, activation, retention, monetization and PLG dynamics.
- A strong statistical foundation.
- A proven record of working end-to-end on your own, from data engineering through modeling to a delivered recommendation.
- Strong hands-on experience using Python for data analysis, experimentation, machine learning and data modeling in a production environment.
- Comfort working with large, messy, event-level datasets.
- Hands-on experience building, validating and deploying predictive models (e.g., classification, forecasting, segmentation) that were used to make real business decisions.
- Demonstrated ability to influence business strategy through data and drive alignment across cross-functional stakeholders, including executive leadership.
- Strong communication: you can turn complex analysis into a clear, compelling story and a concrete recommendation.
- Experience with modern data tooling (Snowflake/BigQuery, dbt, Airflow, and BI tools like Omni/Looker/Tableau).
- BSc in Statistics / Math / CS or equivalent.
Nice to Have:
- Experience at an API-first, developer-focused or PLG company.
- Experience with product analytics platforms (PostHog, Amplitude, Mixpanel).
- Experience with pricing and packaging analysis or usage-based billing models.
- Familiarity with LLMs / AI tooling, and using AI agents to speed up your own analytical work.
- Evidence that you build things on your own: a GitHub profile, portfolio, side projects or publications.
- MSc / PhD in Statistics / Math / CS or equivalent.
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams