Data Scientist – Search

• Solid experience in Search, recommendation systems, or ranking in production

• Fluent Python and the ability to write clean, maintainable code, not just notebooks

• Advanced SQL and experience working with large volumes of data

• Knowledge of NLP and LLMs applied to search and personalization

• Demonstrable track record of models deployed in production with real impact

• Education in a quantitative field (Engineering, Mathematics, Statistics, Physics) or equivalent experience

Experience in e-commerce or marketplace environments, and familiarity with MLOps tools (MLFlow, Flyte, Kubeflow, Feast), is valued.

📍 Barcelona, hybrid model (min. 6 days/month in office, the team self-organizes)
💰 €59,000-70,000 gross/year + 5-10% bonus + very comprehensive benefits (medical insurance, training, Urban Sports, 26 vacation days, top hardware, among others)

They are looking for a Data Scientist to reinforce the Search team. The objective is to improve the platform's search engine and recommendation systems, with a direct impact on conversion and revenue. When users don't find what they are looking for, they leave. This role exists to solve that.

It is an end-to-end role: you define the problem, work the data, train and deploy models in production, and follow up with A/B testing and dashboards. This is not an environment where models stay in staging.

You will work within the Data Science team (7 people with a Manager), alongside a Senior already focused on Search, and in close collaboration with Product Managers, Engineers, and UX Researchers. A cross-functional dynamic where Data Scientists participate in product decisions, not just technical ones.

• Improve the search engine: relevance, ranking, and reduction of queries without results

• Develop and maintain recommendation models, similar items, and personalization

• Apply NLP and LLMs to query understanding and semantic search

• Manage the full cycle: ETL, modeling, training, deployment, and monitoring

• Validate models through A/B testing and build monitoring dashboards

• Explore new technologies and propose PoCs with potential product impact

Python, SQL, Elasticsearch, hybrid semantic search, AWS (SageMaker), Pandas, Scikit-learn, NLP, embeddings, LLMs, MLFlow/Flyte.