Data Scientist

Open position

Stockholm, Sweden·Full-time

[THE ROLE]

What this work looks like

We hire data scientists who put models and experiments into the world, not ones who stop at a notebook. You'll work on client problems and venture products where the question is rarely "can we train a model" and almost always "will this change a decision, a process, or a metric we care about."

That means statistical rigor, experimental design, and the discipline to say when machine learning is the wrong tool.

What you'll do

Frame messy business problems as experiments, estimates, or models with a clear success criterion.
Build and evaluate applied ML systems, classical and modern, with the statistical backbone to defend or kill a result.
Design experiments and causal analyses that survive contact with product, operations, and executives.
Work with engineers to get models and insights into production, not parked in a slide.
Advise clients and founders on where data science creates leverage, and where it does not.

What we're looking for

Strong statistical and experimental foundation: inference, uncertainty, and causal thinking, not just accuracy scores.
Production-minded ML: you have shipped models or decision systems past the prototype.
Python as a working language, plus the data stack needed to get to a trustworthy dataset.
You can explain a result, a limitation, and a recommendation to a technical and a non-technical room.
Bias for action: you iterate with real data and real users rather than waiting for a perfect corpus.
Fluent with modern AI tooling for analysis, experimentation, and development—not as a substitute for rigor.

Nice to have

Causal inference or econometrics in an applied setting.
Experience with LLMs as a modeling or tooling layer, not as a substitute for rigor.
Consulting, product, or founder-adjacent work.
Domain experience in industries we serve.

[HOW WE WORK]

How this role fits Vantir

Vantir data scientists have backbone. You will disagree when a demo is dressed up as a result, and you will commit when the team decides. Ownership here means the outcome of the decision, not the elegance of the notebook. You'll work alongside people who build, and you will be expected to do the same.

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Data Scientist

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