Lovable
Data Scientist, Agent

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TL;DR
You own how we measure and improve Lovable's AI agent. You build the eval systems and experiments that tell us whether a change makes the agent better or worse, and you turn agent telemetry into the fixes that raise success rates and cut errors.
About Lovable
At Lovable, data scientists are not isolated model-builders; they sit close to the product, experimenting continuously with how intelligence changes user behavior and product dynamics.
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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 data scientist who wants to make an AI agent measurably better, not just report on it. You own agent quality metrics and drive them up.
- Experience or strong interest in LLM evaluation and observability: building evals, scoring outputs, tracing agent behavior, and catching regressions.
- Strong SQL and Python, applied statistics, and experimentation.
- Comfortable designing A/B tests for agent changes where outcomes are noisy.
- You build systems and agents that produce this insight continuously, rather than one-off analyses.
- Instinct for what "good" looks like in agent behavior (success, error rates, task completion) and how to measure it when there is no clean answer key.
- Entrepreneurial. Thrives in ambiguity, and works closely with the engineers building the agent.
What You'll Do
- Define and own the metrics for agent quality: success, completion, error rates, and the behaviors that drive them.
- Build the eval systems and experiment framework that decide whether an agent change ships, like an A/B-tested rollout that catches a change increasing errors before it reaches everyone.
- Turn agent traces and telemetry into concrete fixes, working directly with the agent engineering team.
- Build the tooling and agents that produce these evaluations continuously as the agent evolves.
- Set the bar for how we judge agent behavior where there is no answer key to check against.


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Our Tech Stack
We're building with tools that both humans and AI love:
- Languages: SQL and Python
- LLM evaluation & observability: Braintrust, OTEL tracing, many LLM providers
- Warehouse & events: BigQuery, PubSub
- Analytics & product: Hex, Lovable Apps
- Experimentation: A/B and growth testing
- 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.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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