• Design and implement automated data flows to integrate data and processes into the contracting authority’s systems and databases (historical data migration and new ETL flows); • Performance monitoring and improvement of existing and new ETL flows; • Ensure data quality is properly defined, documented, analysed and monitored; • Support data analysis, modelling and visualisation in close collaboration with internal stakeholders, such as disease surveillance experts and fellow data managers.

• Build, train, and deploy machine learning models and data pipelines using Python, pandas, NumPy, and scikit-learn, ensuring scalability and reliability in production. • Design, implement, and continuously improve LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines, including prompt engineering, embedding strategies, vector store integration, and evaluation frameworks. • Build and iterate on agentic AI systems that leverage tool use, multi-step reasoning, and orchestration frameworks to automate complex workflows and decision-making processes. • Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies that inform business decisions and product strategy. • Design and build data visualizations and dashboards that communicate key metrics and insights to both technical and non-technical stakeholders. • Diagnose and resolve complex data and model issues, minimizing drift and continuously improving pipeline efficiency and model accuracy. • Drive technical excellence through rigorous model evaluation, identifying opportunities for improvement, and enforcing best practices in code quality, experiment tracking, and reproducibility. • Collaborate closely with cross-functional teams to translate business requirements into data-driven insights, models, and high-quality analytical solutions.

• Traducir necesidades de negocio en soluciones de analítica avanzada. • Diseñar y desarrollar modelos predictivos utilizando técnicas de Machine Learning. • Utilizar técnicas estadísticas para construir modelos que soporten decisiones de negocio. • Validar modelos y resultados junto al negocio para mejorar desempeño e interpretabilidad. • Liderar la priorización de tareas del equipo de datos.

• Evaluate the technical feasibility of AI infrastructure opportunities across the Americas before Nebius commits capital. • Lead multidisciplinary engineering due diligence across powered land and build-to-suit developments. • Partner with Site Selection, Engineering, Energy, Legal, and Project Development to identify technical risk, assess development feasibility, and support investment decisions. • Lead multidisciplinary technical due diligence for powered land, build-to-suit, and other strategic infrastructure opportunities. • Define the scope of engineering, environmental, geotechnical, surveying, utility, and entitlement studies required to evaluate prospective sites. • Procure, manage, and coordinate external engineering and technical consultants from scope development through final recommendation. • Interpret technical findings and provide development recommendations supporting acquisition decisions. • Prepare technical due diligence summaries and present risks, constraints, and recommendations to executive leadership.