Senior Product & PLG Analyst (Full-Stack Data Scientist)

About Tavily

In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.
Tavily provides the search and intelligence layer that powers AI agents and retrieval systems with trustworthy, real-time information. We work with some of the most innovative teams in AI, from fast-growing startups to global enterprises, helping them build agents that are not only intelligent but informed.

About the Role

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.

This role is fully independent. When a question comes up, you take it all the way: from building the pipeline, model the data, train and deploy the model, analyze the results and come back with a clear recommendation. Nothing waits in a queue for someone else. You'll also work closely with the other analysts on the team, share methods and standards, and collaborate across Product, Engineering, Growth, Marketing and GTM.

What You'll Do

• 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.

What We're Looking For (Must-Haves)

• 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.