Senior Data Scientist

Job DescriptionWe 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. Why is this one different? You define the direction. Architecture, model strategy, evaluation approach — these are open questions, and they become yours. You are the LLM authority for the squad, not a contributor to someone else's. Part of the job is raising everyone else's ceiling. Real scale, real consequence. Your models move conversion across Delivery Hero's global platforms. Genuinely unsolved problems. Multilingual UGC at scale, empirical multi-provider model selection, prompt optimisation, and evaluation infrastructure for generative output — none of these have a settled answer here or anywhere. Agentic development is a first-class part of how we work, used pragmatically to move faster without losing system understanding. Your Mission 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. Prompt strategy that is systematic rather than folkloric. The judgment on when an LLM is the wrong tool. 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. You'll define and operationalise meaningful metrics, and build the evaluation infrastructure behind them — offline eval suites, LLM-as-judge frameworks, and annotation processes — so that evaluation reflects true business value rather than misleading proxies, and a prompt change or model swap becomes a one-day decision. You'll take prototypes to production, shaping architecture and data flows with backend and data engineering, and building the feedback loops that let the system keep improving without constant manual intervention. You'll raise the bar beyond your own work, through best practices, mentoring, and a culture of ownership and pragmatism.