Data Scientist, Pricing

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.

Lovable is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning

an idea into software required so much capital, technical fluency, and time that many ideas never came to life. Lovable is the

counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business

owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on Lovable since its launch in

November 2024. And we’re just getting started.

We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team

is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration.

We look for people who care deeply, ship fast, and are eager to make a dent in the world.

Lovable is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50,

reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next

era of software.

• 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.

• Own the analytics behind pricing and packaging: what we charge, how we package it, and the impact of changes on revenue,

conversion, and retention.