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 PhD 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 PySpark, 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

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