Bolsista Mestre ou Doutor – Cientista de Dados, Graph Neural Networks

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Structure the relationship graph of legal entities by mapping nodes (clients) and edges (corporate links, transactions, guarantees, and suppliers)

• Develop pipelines to transform tabular data into heterogeneous graphs

• Design and train Graph Neural Network architectures, such as GraphSAGE, GAT, and GIN

• Utilize PyTorch Geometric or DGL to propagate risk signals throughout the network

• Conduct comparative experiments between GNN models and traditional models, such as XGBoost and logistic regression

• Validate the evolution of the Gini metric and ensure temporal robustness through backtesting against data leakage

• Apply explainability techniques, such as GNNExplainer, to identify influencers within the graph

• Ensure transparency and compliance with banking governance and compliance standards

• Industrialize the model through FastAPI APIs and batch inference

• Containerize the solution using Docker and Kubernetes for integration with credit decision systems

⛳️ Requirements

• Completed master's or doctoral degree

• Background in Physics, Mathematics, Computer Science, Statistics, Data Science, or related Engineering fields

• Experience in machine learning and deep learning research

• Proficiency in graphs and deep learning applications involving graphs, particularly GNNs

• Programming language: Python

• Publications in deep learning, graphs, CNNs, and GNNs are preferred

• Research experience involving large data volumes is desirable

• Research experience involving multidimensional edge graphs is desirable

• Availability for 40 hours per week

🏝️ Benefits

• Scholarship of R$ 7,000.00 for completed master's or R$ 9,000.00 for completed doctorate

• Remote work format

• Availability of 40 hours per week

• Duration of 12 months