• Build and implement analytical football models, providing valuable insights to enhance our football analytics offerings • Conduct comprehensive data analysis to extract actionable insights • Ensure seamless deployment of machine learning and statistical models using Python, Databricks, and Spark • Handle large datasets efficiently using Spark and Databricks, ensuring data integrity and performance optimization • Work closely with other data scientists, analysts, and stakeholders to understand their needs and deliver high-quality analytical solutions • Identify and implement new tools, techniques, and best practices to enhance our data analytics capabilities

• Reporting to the Director, State Product Analytics, leads the day-to-day delivery of the State Product Management (SPM) Product insights ecosystem • Manages a team of analysts and is accountable for producing reliable, decision-ready Product performance insights • Ensures insights are fast to access, easy to interpret, and consistently correct • Partners cross-functionally to embed insights into SPM decisions • Leads production and continuous improvement of SPM Product insight products • Manages recurring performance deliverables (weekly/monthly/quarterly)

• Design and implement safety architectures for Agentic AI systems, including guardrails, reward modeling, and self-monitoring capabilities • Lead and collaborate on alignment techniques such as inverse reinforcement learning, preference learning, interpretability tools, and human-in-the-loop evaluation • Develop continuous monitoring strategies for agent behavior in both simulated and real-world environments • Partner with product, legal, Responsible AI, governance, and deployment teams to ensure responsible scaling and deployment • Contribute to and publish novel research on alignment of LLM-based agents, multi-agent cooperation/conflict, or value learning • Proactively identify and mitigate failure modes, e.g., goal misgeneralization, deceptive behavior, unintended instrumental actions • Set safety milestones for autonomous capabilities as part of deployment readiness reviews

• Colaborar con equipos de negocio para idear soluciones de Data Science a retos complejos. • Realizar EDA con datos provenientes de Data Lakes y plataformas Big Data. • Desarrollar, mantener y mejorar modelos de: Machine Learning Deep Learning Computer Vision (Must Have). • Entrenar y optimizar modelos usando técnicas como hyperparameter tuning. • Construir pipelines para despliegue e integración de modelos (MLOps es un plus). • Comunicar insights, resultados y hallazgos a stakeholders y product owners. • Trabajar en ambiente Agile/Scrum (standups, planning, sprints).