• 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

• Own end-to-end experimentation and analytical workflows that help the business learn fast and make confident decisions rooted in data. • Work closely with product, data engineering, and business stakeholders to start with customer and business goals, and work backwards to design the right experiments, features, metrics, and models. • Focus initially on optimizing our data pipeline for media data (audio and text) — helping improve throughput, quality, and utility of data as it flows through our systems. • Contribute to the evolution of our data infrastructure to support more advanced capabilities — including eventual creation of novel datasets and internal modeling workflows (e.g., for voice or multimodal data). • Design, analyze, and interpret experiments across tools/services/technologies to identify what works best — producing actionable insights that influence product direction, operations, and future research. • Translate complex analytical outcomes into clear business insights and product recommendations, occasionally contributing to broader thought leadership and research narratives.

• Translate ambiguous business problems into data science problems. • Analyze complex datasets to inform or make decisions. • Apply scientific principles and concepts to support significant invention. • Proactively identify opportunities and solutions to problems. • Create metrics and measurements to quantify the business impact of the work. • Write accurate and clear technical documentation and reports. • Communicate the approach and outcomes clearly with non-technical stakeholders. • Collaborate with stakeholders across a broad set of functions.