Data Scientist
Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("All roles in Stockholm, Sweden"), this listing's salary midpoint is about 94% lower. The offer sits below the benchmark range (€3,904–€12,918). The listed pay band (€477–€613) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 13 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| Market Average: Data Scientist | €3,180/per month | €5,775/per month | €8,713/per month |
| All roles in Stockholm, Sweden | €3,904/per month | €7,051/per month | €12,918/per month |
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.
Job Details
Responsibilities
- 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.
Requirements
- A business-minded data scientist who owns financial analytics outcomes
- Expertise in financial modeling, forecasting, and scenario analysis
- You build systems, not one-off spreadsheets
- Strong SQL, Python, and applied statistics
- Deep curiosity about what drives growth, retention, and revenue
- Entrepreneurial
Skills & Technologies
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