Data Scientist, Finance
TL;DR - You own the data and models behind Lovable's financials. You turn revenue, cost, and usage data into forecasts, scenario analyses, and metrics frameworks the company steers by, build the financial data models and the systems that keep them current, and give leadership and the board numbers they can trust, not one-off spreadsheets.
Why Lovable?
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.
What we’re looking for
A business-minded data scientist who owns financial analytics outcomes: you build the metrics frameworks, data models, and forecasts leadership decides on, instead of handing numbers to someone else to act on.
Expertise in financial modeling, forecasting, and scenario analysis: P&L, cash flow, and unit economics (LTV, margin, token and infrastructure cost).
You build systems, not one-off spreadsheets: production-grade financial data models with testing, clear ownership, and lineage, plus the agents that keep them current.
Strong SQL, Python, and applied statistics. Comfortable with data at scale, and with turning it into forecasts and clear narratives.
Deep curiosity about what drives growth, retention, and revenue, and how the business’s economics actually work.
Entrepreneurial. You thrive with autonomy and ambiguity, work closely with Finance, Data, and Engineering, and can make complex numbers clear to executive and board audiences.
What you’ll do
Own Lovable’s financial analytics: build and maintain the projections, decision analyses, and long-range plans the company steers by, in partnership with Finance.
Build production-grade financial data models and metrics frameworks on the warehouse, with testing, ownership, and lineage, so billing, revenue, and cost data is trusted by default.
Turn product and business data into financial insight: cohort economics, predictive models, and the drivers behind revenue and retention.
Build the dashboards, alerts, and automated reporting, and the agents behind them, that keep Finance and leadership aligned without manual refreshes.
Translate complex financial data into clear narratives for executive and board audiences.
Support fundraising and strategic initiatives with reliable analysis and data-room preparation.
Our tech stack
We're building with tools that both humans and AI love:
Languages: SQL and Python
Warehouse & events: BigQuery, PubSub
Analytics & reporting: Hex, Lovable Apps
Cloud: GCP
And always on the lookout for what's next.
How we hire
Fill in a short form and jump on an intro call with our recruiting team
A call with the hiring manager
A take-home case study
A Most Impressive Project session
Cross-functional interviews with the people you’d work with
A final conversation with leadership
About your application
Please submit your application in English. It's our company language, so you'll be speaking lots of it if you join.
We treat all candidates equally. If you're interested, please apply through our careers portal.