• Support the management and organization of the squad's activities and the product backlog • Track requests, update status, and ensure smooth delivery flow • Work closely with Development and Architecture teams to identify blockers and support their resolution • Participate in agile ceremonies and rituals, promoting alignment across involved areas • Gather, organize, and document relevant information for product evolution • Ensure product documentation is up to date and accessible to teams • Communicate initiative progress, share information, and collect stakeholder feedback • Support backlog prioritization and maintenance, removing obsolete items and aligning requests with business value • Analyze customer and user feedback to identify improvement opportunities • Monitor product indicators and metrics, supporting quantitative and qualitative analyses for decision-making • Contribute to continuous improvement initiatives for processes, products, and user experience

• We are looking for a Product Manager to build and evolve NDD’s financial vertical, focusing on solutions related to a Payment Institution (PI). • The person will be responsible for leading the strategy and evolution of financial products. • Connect customer needs, business vision, regulatory requirements and technical capabilities. • Lead discovery and delivery of financial products and digital solutions. • Define and structure the vision, strategy and roadmap for the financial vertical’s products. • Translate business objectives into prioritized, value-driven initiatives. • Map market opportunities, competitors and trends in the financial sector. • Work closely with multidisciplinary teams (technology, UX, compliance, operations and commercial). • Define and track product metrics and performance indicators. • Support the development of the financial vertical’s value proposition. • Ensure alignment between regulatory requirements and product evolution. • Facilitate ceremonies, refinements and strategic alignments with stakeholders. • Participate in the preparation of business cases and feasibility analyses. • Support decisions related to integrations, financial partners and the payments ecosystem.

• Drive the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images). • Lead the development and validation of novel digital endpoints. • Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements. • Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification. • Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making. • Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases. • Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies. • Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

• Propor, desenvolver e implementar modelos de Machine Learning robustos, escaláveis e performáticos de ponta a ponta; • Implementar modelos de recomendação e personalização; • Desenvolver sistemas de inteligência híbridos, combinando Machine Learning e IA generativa; • Liderar tecnicamente projetos e iniciativas, garantindo a entrega de soluções de alta qualidade; • Interagir com áreas de negócio e de produto, para definir soluções através de uma visão sistêmica e abrangente; • Conduzir análises complexas, extraindo insights valiosos e gerando impacto positivo nos negócios; • Desenvolver e implementar soluções de monitoramento e mensuração de resultados; • Otimizar o tratamento de dados massivos, garantindo eficiência e precisão; • Apoiar outros cientistas de dados, promovendo um ambiente de aprendizado contínuo; • Colaborar com equipes multidisciplinares para integrar soluções de dados em diferentes áreas da empresa; • Manter-se atualizado com as últimas tendências e tecnologias em ciência de dados e IA, aplicando-as para melhorar continuamente os processos e modelos existentes.