> Markdown version of [/jobs/ext/2891807-data-scientist](https://www.wearedevelopers.com/jobs/ext/2891807-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Scientist - **Location:** Pozuelo de Alarcón, Spain - **Contract:** Permanent contract - **Skills:** Python (Programming Language), Power BI, Azure Machine Learning, Large Language Models, Snowflake, Pandas, Pyspark, Scikit Learn, Xgboost, Plotly, Machine Learning Operations, Streamlit Framework, Databricks - **Published:** September 13, 2026 - **Apply:** https://www.recruit.net/job/data-scientist-jobs/8D0DE72F3FBDEB21 ## About the Role * Analítica y estadística: * Buen dominio de estadística aplicada, validación de hipótesis, tests A/B. * Conocimientos sólidos de métricas y evaluación de modelos. * Modelado clásico y ML: * Regresión, clasificación, series temporales, clustering, modelos de boosting (XGBoost/LightGBM). * Explicabilidad (SHAP, LIME). * Entorno de datos: * Experiencia con Python (pandas, scikit-learn, PySpark). * Trabajo con Databricks y datasets en Blob Storage. * MLOps básico (para colaborar con el Engineer): * Registro de modelos en Azure ML. * Test de inferencia antes de pasar el modelo a producción. * Business & comunicación: * Capacidad de traducir necesidades de negocio en experimentos analíticos. * Storytelling con datos. Visualización: Power BI / plotly) y PoC Streamlit * 2-3 años en DS aplicado, con experiencia end-to-end (de problema a prod) y trato directo con negocio. * Experiencia con pipelines de datos (ADF, Functions). * Experiencia en Snowflake. * LLMs: Conocimiento de conceptos básicos para desarrollar PoC de LLMs + Conocimiento de preparación de datos para LLMs. ## Description Actualmente buscamos un/a Data Scientist para descubrir y priorizar casos de uso con negocio, entrenar modelos reutilizables, y empaquetarlos para producción. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)