• Ensure master data is AI‑ready, supporting machine learning, analytics, and Generative AI use cases. • Collaborate with Data Science and AI teams to: Define training‑data requirements, Prevent bias and duplication in AI inputs, Improve feature quality through high‑quality master data, Implement AI‑assisted data matching, classification, and enrichment where applicable. • Support explainability, lineage, and traceability requirements for AI and ML models. • Enable natural‑language and AI‑driven access to master data for analytics and decision support platforms. • Work closely with MDM platforms and tools. Define and oversee integration patterns between MDM, ERP, CRM, data platforms, and AI pipelines. • Collaborate with data engineering teams on cloud‑based data platforms, APIs, and automation workflows. • Ensure master data availability and consistency across operational, reporting, and AI systems. • Lead and mentor Master Data Stewards and Analysts. • Act as a key stakeholder in enterprise Data & AI initiatives. • Drive organizational adoption of data governance and AI‑driven data quality practices. Communicate MDM and AI value clearly to technical and non‑technical stakeholders.

• Design and build AI Solutions that use machine learning models, including GenAI, NLP, and predictive analytics solutions for healthcare applications • Develop, test, and deploy AI solutions in cloud and on-premise environments, ensuring reliability, scalability, and real-world impact • Work with large healthcare datasets, performing data preprocessing, feature engineering, and model training while ensuring compliance with HIPAA and other regulatory standards • Implement and optimize AI solutions within Gainwell’s existing technology stack, collaborating with engineering teams to ensure seamless integration • Continuously monitor, refine, and optimize AI solutions for accuracy, efficiency, and speed, leveraging MLOps best practices • Stay updated with the latest AI/ML advancements, exploring new technologies and methodologies to enhance solution effectiveness • Ensure AI implementations adhere to healthcare industry regulations, ethical AI principles, and data privacy standards • Identify opportunities to automate workflows and optimize business processes using AI-driven solutions

• Support the execution of tactical and operational initiatives defined in the product strategy; • Monitor subscriber consumption behavior, engagement, and user experience in the product, contributing insights and opportunities to improve the subscriber journey; • Monitor KPIs, analyze product performance, generate hypotheses and propose investigations that support improvement recommendations; • Support data analysis and the creation of executive reports; • Produce research briefings, oversee execution and consolidate findings; • Support analyses and scenario building for discussions around content windows between products; • Produce briefings for CRM actions and communication campaigns, and track their implementation; • Consolidate results from acquisition, engagement and subscriber retention initiatives; • Support integration routines with internal and external partner teams, including participation in cadences and agile ceremonies; • Track request creation in Salesforce, monitor delivery statuses and consolidate inputs for the development of commercial plans.

• Compreender as demandas de negócio e traduzir em tarefas técnicas com apoio do time; • Apoiar em operações de transformação e carga de dados (ETL/ELT); • Apoiar na modelagem de bancos de dados e criação de fontes de dados; • Analisar e interpretar dados utilizando técnicas estatísticas e aprendizagem de máquina; • Comunicar resultados das análises e modelos a partir de relatórios técnicos e ambientes de visualização; • Apoiar na construção e testes iniciais de modelos sob orientação; • Seguir boas práticas de APIs de consumo de modelos; • Participar da documentação e padronização de processos de análise; • Colaborar com squads multidisciplinares; • Apoiar na segurança e integridade dos dados utilizados e padronização de processos na esteira de MLOps; • Executar atividades correlatas.