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Salary analysis
Compared with the selected benchmark ("All roles in Berlin, Germany"), this listing's salary midpoint is about 95% lower. The offer sits below the benchmark range (€1,667–€21,083). The listed pay band (€488–€598) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 10 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 Berlin, Germany | €1,667/per month | €8,010/per month | €21,083/per month |
| Pay in our data — not quoted in ad (Senior) | €488/per month | €543/per month | €598/per month |
About the opportunity We are on the lookout for a Senior Data Scientist to join our Content tribe. We are building the ratings and reviews systems — social proof — that shape how millions of people decide what to order, across dozens of markets and languages. It is a greenfield space: the current system is early, and the interesting decisions have not been made yet. The raw material is the hard kind. Millions of short, noisy, multilingual, contradictory pieces of user-generated text, which have to become something a person can act on in two seconds on a restaurant page. Getting that right is an LLM systems problem: aspect extraction, sentiment, summarisation, model selection across providers, systematic prompt optimisation, and the evaluation infrastructure that tells you whether any change made things better. We are honest about the situation. The team is rebuilding ownership of these systems during a transition, and a lot is undefined. That is the offer: you will not inherit a technical direction; you will set it — and you will set the LLM bar for a team with the appetite and the room to clear it.
Job Details
Responsibilities
- You'll own the LLM systems behind social proof end-to-end — quality, reliability, and coverage across languages, platforms, and use cases — including the unglamorous parts: drift, miscalibration, silent quality degradation, and data issues in production.
- You'll set the standard for how this team builds with LLMs. Model and provider selection based on empirical cost, latency, and quality evidence.
- You'll drive the roadmap through problem discovery, finding high-impact gaps, quantifying the business value, and turning them into scoped initiatives — treating cost of inference as a product decision, not only an engineering constraint.
Requirements
- NLP & LLM systems depth.
- Evaluation rigour for generative systems.
- Observability for LLM pipelines.
Skills & Technologies
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