Role Overview

We are seeking a highly motivated Data Scientist with expertise in machine learning, AI, and advanced analytics to solve complex business problems across industries. The role involves collaborating with clients and cross-functional teams to develop, deploy, and scale data-driven solutions, ranging from traditional machine learning models to cutting-edge Generative AI and LLM applications. You will work on high-impact initiatives, contribute to R&D;, and help organizations unlock value through data and AI. The candidate should be based out of Madrid and open to travel as per business requirements.

What You Will Do
- Key Responsibilities
- Partner with clients and stakeholders to translate business challenges into analytical and AI-driven solutions.
- Perform exploratory data analysis (EDA) to identify trends, insights, and opportunities.
- Design, build, train, and evaluate machine learning, deep learning, and Generative AI models.
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and LLM-based applications.
- Define model evaluation frameworks,



conduct A/B testing, and monitor model performance and drift.
- Deploy data science solutions through APIs, batch pipelines, and cloud-based platforms.
- Optimize model inference performance, scalability, latency, and cost.
- Build accurate, explainable, and responsible AI solutions while minimizing bias and ensuring reliability.
- Collaborate with data engineers, software engineers, product managers, and designers to integrate solutions into production systems.
- Present insights and recommendations to technical and business stakeholders in a clear and actionable manner.
- Contribute to internal innovation initiatives, research projects, and thought leadership activities.

What You BringRequired Qualifications
- Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline..
- Strong p

📌 Data Scientist (Madrid)
🏢 StatusNeo
📍 Madrid