Data Scientist, Pricing

Job summary

Responsibilities

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.

• 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.

What they'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.

Tech stack

• SQL
• Python
• BigQuery
• GCP