• Responsible for the company's Data department • Oversee data studies and analyses • Ensure compliance with agreed deadlines • Team development • Improve the product with a data-driven perspective • Understand the importance and urgency of client requests • Monitor prioritization progress • Propose improvements to outcomes • Organize the department's workflows • Participate in internal and external company alignments • Study and understand new types of fraud to generate insights and propose improvements

• Develop end-to-end AI solutions from discovery through deployment and monitoring, including modeling (NLP/ML/LLMs), APIs and integrations. • Design and evolve chatbots and conversational channels using NLP/NLU, driving continuous improvement. • Build integrations via APIs/JSON, microservices and event-driven architectures, and implement MLOps with pipelines, versioning and observability. • Analyze conversational data and recommend actions based on KPIs, ensuring quality through testing and validation. • Work in agile methodologies, collaborating with business, design, engineering and security teams.

• Steward core data assets: Maintain and steward the data that powers Atlas. Contribute to pipelines and infrastructure alongside engineers, and own data quality and integrity long-term. • Design data models: Develop and maintain foundational data models, tables, metrics, and transformations that serve as source of truth for the data product. • Be the data expert: Develop deep expertise in the civic data that powers Atlas, including metadata and data contracts. Understand what partners and internal teams need, and find ways to make data more accurate, accessible, and usable. • Support data consumers: Help researchers, product owners, and other downstream consumers understand data logic and how data flows through Atlas. Build relationships across teams to ensure collaborative problem-solving. • Resolve data issues: Investigate and fix issues when they arise, coordinating with internal stakeholders and external parties (e.g., data vendors, technology partners, etc.). • Improve operations: Proactively identify opportunities to enhance data quality, coverage, or timeliness. Find ways to make data processes more efficient and implement improvements to tools and methodologies.

• Build Predictive Models: Develop and refine machine learning models to solve critical business problems, such as predicting patient churn, estimating operational volume, or scoring advocate quality. • Drive Forecasting Rigor: Own the development of time-series forecasts that guide our capacity planning and resource allocation. You will help us anticipate "what comes next" with higher accuracy. • Design & Analyze Experiments: Lead the design and analysis of A/B tests and causal inference studies. You will ensure we measure the true impact of our product and operational changes. • Uncover Deep Insights: Apply advanced statistical methods to complex datasets to find patterns that simple reporting might miss. You will answer the "why" and "how" behind our most difficult questions. • Productionize Solutions: Work closely with Data Engineers to take your models from a local notebook to a production environment, ensuring they run reliably within our data infrastructure. • Communicate Complexity: Translate complex statistical findings into clear, actionable narratives for non-technical stakeholders. You will be the voice of statistical reason in strategic discussions.