Vibe

Data Scientist

Paris, Île-de-France, FranceFull timePosted 12 days ago
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Senior Data Scientist — Performance Team

About the Role:

You'll join the Performance team, the engineers and scientists who own advertiser outcomes at Vibe: prediction models, bidding, and the delivery stack that drives both.

This role exists because our modeling roadmap is bigger than our current bandwidth. You will help to build the core prediction architecture that powers every bid, and the scale and diversity of data available to us is about to grow significantly.

We need someone who can turn that into measurable advertiser performance, not just publishable ideas.

Reasons to join:

1. CTV performance advertising is still being invented. Cross-device attribution, identity graphs linking TVs to households — the modeling problems are nothing like web display or search. There's no playbook to copy; you'd be writing it

2. You'll work on data few others have. We're building toward crossing CTV signal with real-world purchase and behavioral data: joining what people watch with what they actually buy. Measurement, incrementality, training data: all of it changes

3. The timing is right. Vibe is one of the fastest-growing CTV companies out there. Early enough that the model architecture is yours to shape. Late enough that your work ships to real advertisers and shows up in the numbers within weeks

What You'll Do:

- Own the prediction stack

- Improve our unified multi-task model: one architecture predicting visits, purchases, and other outcomes simultaneously, with heads learning from each other through shared representations to handle sparse conversion funnels and enable more precise bidding decisions

- Take ideas from exploration to production yourself: dataset design, architecture, training, debugging (gradients, convergence, etc.), deployment, and monitoring for drift and regression. You will work closely with Performance engineers building the serving and bidding infrastructure

- Design new features from raw signal, including household-level fe...