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. Required Qualifications Bachelor's, Master's, or Ph D in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline.



Strong programming skills in Python and SQL. Hands-on experience with machine learning frameworks and the Python data science ecosystem. Experience developing predictive, classification, optimization, and statistical models using structured and unstructured data. Understanding of Generative AI, LLMs, prompt engineering, RAG architectures, and model evaluation techniques. Experience with model deployment, monitoring, testing, and productionization. Strong problem-solving and analytical thinking skills. Excellent communication and stakeholder management abilities. Preferred Skills Experience with big data technologies such as Py Spark, Hadoop, Hive, or similar frameworks. Familiarity with Databricks, Airflow, Kedro, Dask, RAPIDS, or comparable data engineering tools. Knowledge of cloud platforms including AWS, Azure, or GCP. Experience with containerization and orchestration tools such as Docker and Kubernetes. Exposure to software engineering best practices and MLOps. Experience deploying AI and analytics solutions in enterprise environments. Consulting or client-facing project experience is highly desirable. Skills: pyspark,sql,python #J-18808-Ljbffr

📌 Data scientist (Madrid)
🏢 Virtual Connect Solutions
📍 Madrid