• Analyze complex data and extract insights that create value for the business and customers • Develop reports, dashboards, and data visualizations to communicate results • Identify trends, patterns, and anomalies in data to support strategic decision-making • Collaborate with data engineering teams to ensure data quality, integrity, and accessibility • Create and document performance metrics and key indicators to monitor processes • Support the definition of requirements and specification of data-driven solutions • Conduct feasibility studies and impact analyses of proposed solutions • Communicate analytical findings clearly and concisely to diverse stakeholders

• Analyze complex data and extract insights that generate value for the business and customers • Develop reports, dashboards and data visualizations to communicate results • Identify trends, patterns and anomalies in data to support strategic decision-making • Collaborate with data engineering teams to ensure data quality, integrity and accessibility • Create and document performance metrics and key indicators to monitor processes • Support the definition of requirements and specification of data-driven solutions • Conduct feasibility studies and impact analyses for proposed solutions • Communicate analytical findings clearly and concisely to various stakeholders

• Liderar la estrategia de modelado de datos, visualización y habilitación analítica. • Diseñar arquitecturas de BI y capacitar a los stakeholders. • Actuar como referente técnico para Analytics Engineers y elevar la calidad técnica del equipo. • Colaborar con líderes de Producto, Negocio, Ingeniería y Data Science para anticipar necesidades de datos. • Coordinar iniciativas de datos que cruzan múltiples squads.

• Create and extend applied AI and ML solutions that enable intelligent, real-time decisions • Collaborate with engineering, product, and domain experts • Execute end-to-end data science tasks • Design, train, and evaluate machine learning and deep learning models • Apply techniques across computer vision, pattern recognition, predictive modeling, and generative AI • Translate ambiguous requirements into deployable systems • Build scalable data pipelines and feature engineering workflows • Partner with engineering to integrate models into production • Implement monitoring, validation, and retraining strategies • Use statistical methods to assess model quality