Especialista Cientista de Dados

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Especialista Cientista de Dados based in Brazil. We are looking for a Data Scientist Specialist to develop and advance Artificial Intelligence solutions focused on Computer Vision, transforming images and videos into actionable insights that support business decisions and create value for customers. In this role, you will lead the end-to-end Machine Learning lifecycle, from data exploration and preparation to model deployment and production monitoring. You will design, train, and optimize deep learning models using modern architectures and techniques while ensuring performance, scalability, and reliability. Working with cloud infrastructure, GPU computing, and MLOps practices, you will help bring innovative AI solutions into production. You will collaborate with multidisciplinary teams across product, engineering, and business functions, contributing technical expertise, continuous improvement, and knowledge sharing in a dynamic, data-driven environment.

Develop Computer Vision models for object detection, classification, segmentation, and tracking in images and videos. Design, train, and optimize deep neural networks using modern Deep Learning architectures, including CNNs, Transformers, and generative models. Build and maintain data preparation, processing, annotation, and augmentation pipelines to ensure the quality and reliability of visual datasets. Conduct experiments and model evaluations, establish performance metrics, validate results, and monitor model effectiveness. Apply transfer learning, fine-tuning, and hyperparameter optimization techniques to improve model accuracy and efficiency. Write scalable, high-quality Python code following software engineering best practices, including version control and testing. Implement MLOps processes for model versioning, packaging, deployment, and monitoring in production environments. Optimize and compress models for environments with processing constraints, including edge computing and embedded devices. Leverage cloud platforms and GPU-based infrastructure to train models and process large volumes of data. Collaborate with product, engineering, and business teams to understand challenges, design effective solutions, and deliver measurable value through AI and data. Keep solutions evolving by monitoring research, emerging technologies, and industry developments in Artificial Intelligence, Machine Learning, and Computer Vision. Share technical knowledge and contribute to the professional development and technical growth of other team members. Requirements: Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, or a related field. Strong professional experience in Computer Vision, Machine Learning, and Deep Learning. Advanced proficiency in Python and hands-on experience with PyTorch. Familiarity with TensorFlow/Keras and the broader ecosystem of Data Science and AI libraries. Knowledge of MLOps practices, model deployment, and cloud architectures, preferably using AWS, Azure, or Google Cloud Platform. Experience working with SQL and NoSQL databases and processing large volumes of data. Strong analytical and critical-thinking skills, with the ability to solve complex technical problems. Collaborative mindset and the ability to work effectively with multidisciplinary teams in results-oriented environments. A postgraduate degree or specialization in Artificial Intelligence, Data Science, or a related discipline is considered an advantage. Benefits: An inclusive, people-centered workplace that values diversity, collaboration, and individual contributions. A purpose-driven environment focused on using data and technology to create opportunities and deliver meaningful impact. Opportunities to work on innovative Artificial Intelligence, Machine Learning, and Computer Vision projects. A multidisciplinary environment that encourages technical knowledge sharing and professional development. A culture that supports continuous learning, innovation, and career growth. An emphasis on employee well-being and balancing professional responsibilities with personal commitments. Recognition through workplace culture and employer awards, as described in the original job posting.