A3Data

• Understand business problems that can be solved with data science using statistical analysis and machine learning models; • Ensure integrity in data collection from all sources for all analyses performed by the data team; • Collect and prepare datasets for analysis; • Perform data exploration, test hypotheses, identify insights and validate them with business stakeholders; • Develop statistical and machine learning models; • Deploy models for consumption by the business; • Produce analyses and reports for business areas; • Contribute business-focused ideas to the department's data science analyses.

• Create and deploy data pipelines • Work in a collaborative environment • Tackle diverse challenges • Empower people through data

• Hands-on experience developing, fine-tuning (fine-tuning) and applying Large Language Models (LLMs) to meet specific business needs, with a focus on AWS Bedrock. • Mastery of Natural Language Processing (NLP) techniques for text analysis, extraction, and generation. • Ability to implement a complete data pipeline, from acquisition and processing to refinement and delivery of results. • Select and implement advanced Machine Learning and Deep Learning algorithms, using both supervised and unsupervised models. • Strong analytical ability to solve business problems using data-driven techniques. • Advanced back-end development skills, preferably with FastAPI, and experience with WebSocket communication for real-time solutions. • Develop and maintain recommendation, classification, and prediction models applied to market challenges. • Work using agile methodologies and focus on delivering value to customers. • Develop and present technical and executive reports to justify the techniques adopted and support decision-making. • Stay up to date on new trends in Generative AI, proposing continuous innovations. • We will also be happy if you have: ➕ Experience with MLOps, including monitoring, versioning, and scaling models in production.

• Collect relevant information for the process of creating indicators (KPIs); • Organize information in spreadsheets to facilitate day-to-day analysis; • Review and validate data as it is collected; • Update spreadsheet data to maintain historical records; • Analyze data and create dashboards that help better understand problems and their root causes, enabling the development of targeted action plans; • Participate in project roadmap discussions with the client; • Provide suggestions for improvements and optimizations; • Identify new opportunities and potential projects within the client.

• Develop, evolve and maintain back-end applications in Java 17+, using Spring Boot • Design and implement REST APIs (and occasionally GraphQL), ensuring best practices for design, security and performance • Actively participate in technical refinement of requirements together with product and architecture teams • Contribute to system architecture decisions, especially in microservices environments • Implement and maintain integrations between internal and external systems • Ensure code quality through automated tests, code reviews and engineering standards • Maintain and evolve relational databases, modeling data efficiently • Support observability, monitoring and incident resolution in production • Collaborate with the team on CI/CD, automation and cloud initiatives • Mentor less experienced developers and promote best practices within the team • Contribute to continuous improvement of processes, application performance and reliability.

• Empacotar e versionar modelos de machine learning (MLflow, SageMaker Model Registry) • Definir e implementar serviços AWS adequados (SageMaker, Lambda, ECS/EKS, API Gateway, entre outros) • Construir e manter esteiras CI/CD, garantindo automação de testes, build e deploy • Automatizar deploys em múltiplos ambientes (dev/staging/prod) com segurança e rollback • Expor modelos para consumo por outros serviços (via endpoints ou Lambdas) • Configurar e acompanhar monitoramentos em produção (CloudWatch, logs, métricas) • Colaborar com times multidisciplinares para garantir soluções eficientes, seguras e escaláveis.

• Develop, program, and test machine learning systems • Design architectures for reproducible, scalable, and monitored Machine Learning solutions • Assist the data science team in designing and building model deployment pipelines • Build data engineering processes/pipelines within the data environment • Define architecture standards, procedures, and tooling • Define data layers and their intended usage • Implement, fine-tune, and monitor LLM models (e.g., Bedrock, OpenSearch, HuggingFace) • Build inference pipelines for RAG-based responses (Retrieval-Augmented Generation) • Ensure versioning and reuse of trained models and embeddings • Work on continuous improvement of response relevance and source ranking • Work on reducing token costs using optimization techniques • Support integrations with AI APIs, vector stores, and document repositories • Communicate information clearly with technical and business team members • Help identify potential errors and report inconsistencies

• Define and implement scalable, reproducible, monitorable, production-ready Machine Learning architectures. • Develop, evolve, and maintain production Machine Learning pipelines and services, ensuring reliability and performance. • Deploy highly available models and pipelines with a focus on MLOps, CI/CD, and automation. • Collaborate with data scientists, data engineers, developers, and business stakeholders. • Diagnose and resolve complex issues related to models and pipelines in production. • Lead technical discussions and workshops, and support architectural decisions with teams and clients. • Contribute to raising the client's and A3 Data's technical maturity by promoting best practices.

• Lead end-to-end Data Science projects using agile methodologies with a focus on delivering business value. • Perform exploratory analyses to identify patterns, trends and generate actionable insights to support decision-making. • Develop, evaluate and implement statistical, predictive and prescriptive models (supervised and unsupervised). • Act in a consultative manner to understand client pain points and needs throughout the project. • Present results, analyses and recommendations to technical and non-technical stakeholders with clarity and storytelling. • Collaborate actively with the team, sharing knowledge, best practices and new technical approaches. • Support internal requests and strategic initiatives when needed.

• Lead end-to-end Data Science projects using agile methodologies with a strong focus on delivering business value. • Perform exploratory data analysis (EDA), identify patterns and trends, and generate actionable insights to support decision-making. • Develop, evaluate, and implement statistical, predictive, and prescriptive models (both supervised and unsupervised). • Act in a consultative manner to understand client pain points and needs throughout the project. • Present results, analyses, and recommendations to technical and non-technical stakeholders with clarity and storytelling. • Actively collaborate with the team, sharing knowledge, best practices, and new technical approaches. • Support internal requests and strategic initiatives as needed.