Neon

• Design, guide, and ensure the scalability of the app's Home screen architecture, serving as the technical reference for the team and positively influencing the company's engineering. • Lead the transition and strategic adoption of modern technologies, with a special focus on building a hybrid environment. • Promote development best practices, ensuring high test coverage (unit, integration, and automated tests), rigorous code reviews, and mentorship for senior and mid-level engineers. • Closely monitor the app's health in production (performance metrics, bugs, crashes, and latency), ensuring resilience in a mission-critical (fintech) environment. • Collaborate closely with PMs, Designers, and stakeholders to translate complex business challenges into elegant, performant, and feasible technical solutions.

• Monitorar resultados e indicadores de performance do funil de clientes, implementando dashboards e automatizações para esses acompanhamentos. • Liderar estratégias de aquisição de produtos e engajamento, focando na conversão de clientes novos e existentes da Neon dentro e fora do aplicativo, através de canais como Push, E-mail e WhatsApp. • Realizar análises de dados profundas para a geração de insights e identificação de novas oportunidades de negócio. • Avaliar novos projetos, produtos e *features*, auxiliando diretamente na priorização dos roadmaps junto aos times de engenharia e produto. • Desenhar e liderar testes de growth e experimentos de ponta a ponta, sendo responsável desde a geração da hipótese e delineamento do experimento, até a execução, mensuração e comunicação dos resultados. • Apresentar resultados e teses de crescimento estruturadas em fóruns de liderança, garantindo o alinhamento estratégico das iniciativas.

• Your mission is to turn Neon's raw consumer audio streams into the cleanest, most reliable training data on the market, and to build the commercial and operational engine that gets it into the hands of the world's leading AI labs. • As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing community of 500,000+ mobile users and delivers production-ready datasets to frontier labs. • In practice, that means three things above all: - Structuring and managing the data deals that turn our recordings into revenue - Holding every dataset to a quality bar that keeps buyers coming back - Standing up human transcription, annotation and other operations, largely overseas, that make it all possible • You'll work directly with our CEO on commercial priorities and help shape each deal, interface with buyer-side engineering and research teams at frontier labs to translate their exact specifications into deliverable dataset plans, and partner with internal engineering and external vendors to make sure the pipeline supports what we've sold. This is a foundational role: the datasets and processes you build are the product we sell.

• Design and lead the overall card shipping and delivery strategy, mapping and mitigating critical bottlenecks in the chain to ensure cost efficiency (savings) and maximize activation rates, as well as identify the main levers for customer engagement beyond credit and how to influence them. • Work independently on modeling and extracting large volumes of complex data via SQL, transforming raw data into strategic guidance that drives the product growth roadmap. • Develop, validate and present complex financial feasibility studies to leadership, connecting product initiatives directly to business health metrics such as LTV, CAC and churn control. • Implement, structure and roll out an end-to-end testing framework, raising the technical standard of our analyses and ensuring robustness in conclusions and recommendations. • Design and ensure governance of performance dashboards (KPIs) for senior leadership, translating technical metrics and analyses into clear, actionable business narratives. • Architect automation solutions for data collection and processing workflows, ensuring recurring analyses operate with maximum agility, accuracy and autonomy. • Serve as the primary strategic link between product tribes, operations and senior (C-level) leadership, communicating complex strategies persuasively, aligning expectations and removing blockers.

• Own the product that sits in the hands of 500,000+ users • Write code and make big architectural decisions • Solve engineering problems around VoIP and telephony • Integrate real-time communication tools • Ensure app works flawlessly for users around the world

• Monitor and analyze traffic, behavior and conversion metrics across digital channels. • Develop analyses and studies to identify opportunities for organic growth. • Plan, execute and monitor On-Page, Off-Page and Technical SEO strategies. • Conduct technical SEO audits and recommend improvements to site architecture, indexing, performance and user experience. • Track keyword rankings, market share and competitor activity. • Create dashboards and executive reports to monitor results. • Collaborate with Product, Content, Technology and Growth teams to implement improvements. • Ensure the quality of digital measurement through analytics tools and tagging. • Perform funnel analyses, navigation behavior studies and identify conversion bottlenecks. • Support CRO (Conversion Rate Optimization) initiatives based on data and testing.

• Develop and maintain features in Android applications, ensuring performance, quality, and responsiveness. • Collaborate with designers and other developers to create efficient, scalable solutions. • Participate in the technical design of solutions proposed by the team and monitor the health of features in production (bugs and crashes). • Write unit and integration tests to ensure code quality. • Ensure the app works across different device models and operating system versions. • Evaluate solutions proposed by the product team and other stakeholders, considering technical best practices. • Actively participate in team ceremonies and development team activities.

• Desenvolver, testar e colocar em produção modelos tabulares complexos de alta performance, focados estritamente em problemas de classificação e risco de crédito. • Garantir o ciclo de vida completo dos modelos (end-to-end), desde a manipulação e extração de dados brutos até o monitoramento e suporte operacional em ambiente produtivo. • Investigar e intervir de forma ágil e prática em cenários reais de falha ou degradação de modelos em produção, garantindo a resiliência das operações de crédito. • Utilizar técnicas avançadas de interpretabilidade de modelos para traduzir comportamentos estatísticos complexos em insumos claros para a tomada de decisão de negócio. • Atuar como referência técnica avançada (Staff), elevando a barra de engenharia e modelagem de dados de todo o time de cientistas.

• Define and evolve the technical vision and engineering strategy across multiple domains, ensuring alignment with overall business objectives. • Lead, mentor, and develop engineering managers and senior technical leaders, fostering a culture of continuous learning and high performance. • Ensure predictable, high-quality delivery of complex products by managing technical roadmaps and execution risks. • Promote operational excellence through robust SRE practices, observability, and automation across the software lifecycle. • Act as a strategic partner to Product, Data, Risk, and Compliance teams to enable financial innovation in a regulated environment. • Manage the department budget, headcount planning, and strategies for attracting and retaining top technical talent. • Influence the architecture of large-scale distributed systems, ensuring resilience, security, and cost efficiency.

• Develop and architect AI agent systems based on single-agent and multi-agent architectures. • Implement modern communication protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A). • Master the information lifecycle for AI: from strategies like chunking and embeddings to implementing techniques such as RAG (Retrieval-Augmented Generation), reranking, and query transformation. • Manage the complexity of multi-model environments. Evaluate and select the best providers based on trade-offs in latency, context, and cost. Standardize the use of abstraction frameworks to avoid vendor lock-in. • Act as a technical mediator in defining AI strategies across the product portfolio. • Ensure architectural consistency across initiatives, identifying opportunities for componentization and reuse. • Serve as a decision-maker in architectural dilemmas, promoting an AI Engineering culture. • Connect AI technical capabilities to the real pain points of business areas. Establish LLMOps pipelines that include: • Rapid experimentation cycles (A/B testing of prompts/models) • Feedback systems (human-in-the-loop) • Monitoring of business metrics impacted by AI • Systematic evaluation (Evals) - creation of test datasets and frameworks.