Datarisk

• Develop end-to-end features with an emphasis on frontend (React/TypeScript), integrating with backend APIs (Python/FastAPI) that power Artificial Intelligence solutions; • Build interfaces and analytical dashboards that translate data and predictions from Machine Learning models and Generative AI into clear user experiences; • Collaborate with Product and UX Design to transform flows and wireframes into high-quality components, following the product design system and frontend architecture standards; • Troubleshoot and solve software issues autonomously, applying engineering best practices on the frontend and making occasional contributions to the backend; • Maintain high standards for code quality and documentation, actively participating in code reviews; • Contribute to the team’s technical growth by sharing knowledge about the stack and frontend best practices with colleagues.

• Você vai ter aulas e feedbacks individualizados com nossos Líderes de Ciência de Dados • Você terá contato com problemas reais de negócio simulados no ambiente do Programa • Interagir com Cientistas de Dados da equipe Datarisk, em sessões de troca sobre a Ciência de Dados

• Own the product from problem identification to value delivery; • Translate business objectives into clear problems, hypotheses, and solutions; • Define and track OKRs, product metrics, and impact KPIs; • Prioritize the backlog based on value, effort, risk, and real data; • Actively participate in defining the roadmap (short, medium, and long term); • Drive continuous discovery (user interviews, data analysis, and real feedback from CS, sales, and support); • Create hypotheses, validate solutions, and avoid unnecessary build; • Collaborate with Design on flows, wireframes, and rapid validations; • Write clear and concise PRDs; • Detail User Stories, acceptance criteria, and business rules; • Work daily with the engineering team (squads); • Monitor development, testing, rollout, and post-launch; • Ensure quality, correct scope, and value delivery; • Track metrics before and after launch; • Evaluate the real impact of deliveries; • Make data-driven decisions (not just based on intuition); • Adjust the product quickly when necessary; • Define MVPs for rapid Product validation.