• 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).

• Build data products and algorithms that optimize marketing performance—including budget allocation, targeting strategies, and campaign optimization across multiple channels. • Design and implement measurement strategies for marketing initiatives, including attribution modeling, incrementality analysis, and multi-touch attribution. • Drive marketing strategy through data insights, A/B testing, and causal inference methodologies to maximize ROI on marketing spend. • Develop and maintain analytics pipelines and data infrastructure, working collaboratively with data engineers and other data scientists. • Conduct exploratory analysis to identify growth opportunities, understand customer behavior, and optimize marketing tactics. • Provide business-as-usual analytics support including reporting, dashboards, and ad-hoc analysis for marketing stakeholders. • Leverage AI tooling to handle operational tasks efficiently and focus your expertise on high-impact strategic problems. • Partner with ad platforms (Google, Meta, etc.) and internal engineering teams to ensure robust tracking, data integration, and measurement capabilities. • Take ownership and ship it—continuously look for the simplest, most effective way to approach challenges; Occam's razor is your friend.

• Be the dedicated data science partner to the Consolidation product team, owning the analytics and modeling that power product-led growth across onboarding, activation, and full consolidation. • Design and analyze rigorous product experiments across the client journey, from first funding through to full asset consolidation, using A/B testing, non-inferiority tests, and other causal inference methods to measure the impact of product changes on key growth metrics. • Partner with the team's Personalization DRIs to incorporate machine learning models, such as next-best-account recommendations and personalized value propositions, into product flows where they will deliver the highest impact to clients. • Develop deep product analytics to understand how clients move through the consolidation funnel, linking external accounts, reaching their first $5k, adopting multiple products, and identify where the biggest opportunities exist to accelerate that journey. • Create and maintain data models and dashboards that monitor consolidation KPIs (Premium+ conversion, registrant-to-funded rates, linking rates, activation milestones) with a focus on data quality, accessibility, and efficiency. • Collaborate cross-functionally with Product, Engineering, Growth, and Lifecycle Marketing to translate analytical findings into product decisions and shipped features. • Proactively identify and size growth opportunities by being closest to the data, and then partner with stakeholders to get those opportunities prioritized and executed. • Build automation tools and pipelines that increase team efficiency and scalability. • Contribute to the team's culture of learning and knowledge sharing. Teach and learn from your teammates, and help make others successful. • Take ownership and ship iteratively; believe that simple is better and Occam's razor is your friend.