Data Science | SR

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Science | SR based in Brazil.** This is a senior-level opportunity for a Data Scientist to build advanced machine learning and Generative AI solutions with measurable business impact. You’ll own projects end-to-end, from data exploration and experimentation through model validation, deployment, and interpretation. The role combines traditional machine learning with LLMs, RAG, agent-based architectures, and modern AI evaluation techniques. You’ll work extensively with Python, Databricks, MLflow, AWS, Azure, LangChain, and LangGraph in a highly technical environment. You’ll also help develop scalable data and AI pipelines, APIs, vector-search solutions, and cloud-native architectures. The position offers the opportunity to work on complex problems where experimentation, engineering excellence, and practical business outcomes go hand in hand. It is well suited to a senior professional who enjoys solving challenging problems and working at the forefront of AI and data science ### Accountabilities: - Develop predictive models and machine learning algorithms to solve complex business challenges, taking ownership from data exploration and experimentation through validation and interpretation, with a focus on measurable outcomes. - Apply supervised, unsupervised, and deep learning techniques using Python and leading data science libraries, including Scikit-learn, XGBoost, and LightGBM. - Design and execute experiments using tools such as Jupyter, Databricks, and MLflow, ensuring robust model evaluation and reproducible experimentation. - Evaluate the performance of RAG and language-model-based systems using quality, cost, latency, and semantic metrics, including BLEU, ROUGE, and related evaluation approaches. - Build text-ingestion pipelines for vector databases, including solutions using AWS OpenSearch, and develop APIs with LangChain and LangGraph integrated with OpenAI and AWS Bedrock. - Work with AWS services such as S3, Lambda, Parameter Store, and Secrets Manager, while contributing to serverless and containerized architectures across AWS and Azure. - Leverage Azure Document Intelligence and other cloud services for document extraction, processing, and AI-powered workflows. ### Requirements: - Solid professional experience with Python and strong practical knowledge of data science and machine learning. - Hands-on experience developing and deploying machine learning models using libraries such as Scikit-learn, XGBoost, and LightGBM. - Proven experience with Large Language Models (LLMs), Generative AI, RAG architectures, and agent-based AI solutions. - Practical experience working with Databricks and MLflow for data science, experimentation, and model development workflows. - Experience with LangChain and LangGraph for building LLM-powered applications and agentic solutions. - Experience working with AWS Bedrock and familiarity with the broader AWS ecosystem. - Knowledge of language-model evaluation metrics such as BLEU and ROUGE is considered a plus. - Working knowledge of both AWS and Azure cloud environments, including cloud-native, serverless, and containerized architectures. - Strong analytical and problem-solving abilities, with the capacity to translate complex technical challenges into practical solutions. - Ability to work independently, communicate technical concepts clearly, and collaborate effectively with multidisciplinary teams. ### Benefits: - Remote work opportunity for professionals based in Brazil. - Opportunity to work on advanced Machine Learning, Generative AI, LLM, and RAG initiatives. - Exposure to modern AI and cloud technologies, including AWS, Azure, Databricks, LangChain, LangGraph, and MLflow. - Work on technically challenging projects with a strong focus on innovation and measurable business impact. - Opportunity for continuous professional development and exposure to emerging trends in Artificial Intelligence and Data Science. - Collaboration within a technology-driven environment that values knowledge sharing and the development of advanced technical skills. - Competitive compensation and benefits package, according to the employment arrangement and local market practices.