• Atuar no design, desenvolvimento e testes de aplicações web voltadas à sistematização de decisões; • Colaborar na evolução contínua das ferramentas existentes, incorporando feedback e novas tecnologias; • Garantir qualidade por meio de testes unitários, integração e ponta a ponta; • Contribuir para definição e implementação de arquitetura robusta; • Trabalhar com otimização de APIs, caching e melhoria de performance; • Participar do ciclo completo de desenvolvimento, incluindo implantação e manutenção;

Architect and build the decoupled semantic layer that powers RCM Intelligence — extracting metric definitions into a queryable format (MetricFlow/dbt Semantic Layer) that serves dashboards, natural language queries, and agentic systems from a single source of truth Design and develop metric frameworks and root cause analysis capabilities, including metric trees that decompose performance changes (e.g., why did metric X drop?) into their contributing factors — enabling clients and internal technical teams to self-serve answers that today require hours of analyst work Execute ambiguous, complex projects independently, proactively asking for help when needed, and translating client needs into structured analytical solutions Lead and execute sophisticated ad-hoc analyses using Python and SQL, then translate findings into scalable, parameterized data products that serve all clients rather than being rebuilt one-off Develop and optimize production data pipelines in dbt, ensuring consistent metric definitions and data quality across Cedar's Bronze → Silver → Gold architecture Serve as a thought partner to Technical Account Managers and Client Managers, using internal data products to surface proactive insights that strengthen client relationships and reduce reactive fire drills Partner with Data Engineers to implement robust infrastructure that supports the platform's analytical requirements and ensures scalability Present and communicate platform capabilities, analytical findings, and methodology to commercial stakeholders and clients — translating technical concepts for non-technical audiences Identify and advocate for opportunities to enhance Revenue Cycle Management Intelligence's impact, including features that reduce manual analyst work and increase client self-service Cedar was co-founded by Florian Otto and Arel Lidow in 2016 after a negative medical billing experience inspired them to help improve our healthcare system. With a commitment to solving billing and patient experience issues, Cedar has become a leading healthcare technology company fueled by remarkable growth. "Over the past several years, we've raised more than $350 million in funding & have the active support of Thrive and Andreessen Horowitz (a16z). As of November 2024, Cedar is engaging with 26 million patients annually and is on target to process $3.5 billion in patient payments annually. Cedar partners with more than 55 leading healthcare providers and payers including Highmark Inc., Allegheny Health Network, Novant Health, Allina Health and Providence.

• Collaborate with Legal and AI Data Science teams to address challenges in content protection and compliance. • Analyze third-party AI models and applications for potential unauthorized use of Elsevier content. • Support the development and evaluation of AI models for detecting copyright infringements and compliance risks. • Assist the legal team in preparing technical documentation for compliance cases. • Analyze datasets, identify patterns, and communicate findings clearly to stakeholders. • Document issues and suggest improvements to systems and processes. • Learn best practices and advanced methodologies from experienced team members.

• Drive the roadmap for internal AI & ML initiatives, aligning with business objectives and regulatory requirements. • Serve as a subject matter expert on fraud analytics, advising leadership on emerging threats and mitigation strategies. • Architect and deploy cutting-edge machine learning and AI models to maximize fraud recovery, behavioral analysis, and predictive fraud prevention. • Optimize models for scalability, real-time performance, and minimal false positives. • Identify new data sources and develop advanced feature engineering techniques to enhance fraud detection capabilities. • Lead research into novel algorithms, including graph analytics and deep learning approaches. • Partner with Legal, Leadership, and Engineering teams to integrate solutions into enterprise systems. • Communicate complex technical concepts to non-technical stakeholders and influence decision-making. • Guide and mentor junior data scientists, fostering a culture of innovation and continuous learning. • Ensure models adhere to regulatory standards (OCC, PCI DSS) and ethical AI practices.