Data Scientist

TL;DR — You own the data behind how Lovable prices and packages. You turn usage, cost, and willingness-to-pay data into pricing and packaging bets, run the experiments to test them, and build the models that tell us what a change does to revenue and retention. What we're looking for Commercially-minded owner: A data scientist who owns pricing and packaging outcomes. You find the opportunity, model it, test it, and drive the change. Economics fluency: Deep understanding of unit economics including LTV, margin, token and infrastructure cost, willingness to pay, discounting, and subscription or credit models. Technical rigor: Strong SQL, Python, applied statistics, and experimentation skills. You are comfortable with causal questions where a clean A/B test is not always possible. Systems builder: You build the systems that price and package, such as discounting or packaging models that run on user records, rather than just delivering one-off analyses. Strategic judgment: An instinct for the tradeoff between growth and monetization, and the judgment to know when to prioritize each. Entrepreneurial spirit: You thrive with autonomy and enjoy working closely with finance, product, and growth teams. What you'll do Own the analytics behind pricing and packaging: what we charge, how we package it, and the impact of changes on revenue, conversion, and retention. Build models for unit economics, willingness to pay, and price sensitivity, translating them into concrete pricing bets. Design and execute pricing and packaging experiments, and act decisively on the results. Build the systems that operationalize pricing decisions, such as personalized discounting based on user records. Collaborate with finance, product, and growth to sequence and ship pricing changes effectively. Our tech stack We're building with tools that both humans and AI love: Languages: SQL and Python Warehouse & events: BigQuery, PubSub Analytics & product: Hex, Lovable Apps Experimentation: A/B and growth testing Cloud: GCP