Description:
We are looking for a graph machine learning engineer. In this role, you will get to work alongside various experts in product and engineering.
Example Responsibilities:
- Devise and construct cutting-edge graph-based machine learning models and algorithms aimed at detecting and mitigating fraudulent activities within intricate, interlinked datasets.
- Implement and enhance graph-based algorithms dedicated to node classification, link prediction, and community detection, specifically tailored to identify patterns indicative of fraudulent behavior.
- Work collaboratively with cross-functional teams to integrate machine learning models into scalable and efficient production systems.
- Conduct thorough analyses and experiments to evaluate model performance, scalability, and efficiency on graph-based data structures.
- Research and remain abreast of the latest advancements in graph-based machine learning techniques, contributing groundbreaking concepts to augment our fraud detection technological capabilities.
Skills and Experience:
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
- Demonstrated proficiency (3 years) in crafting and deploying machine learning models tailored explicitly for detecting fraud within graph-based data structures.
- Strong proficiency in graph theory, graph algorithms, and graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph).
- Proficient in programming languages commonly used in machine learning (e.g., Python, R, Java) and libraries/frameworks (e.g., NetworkX, PyTorch Geometric, GraphSAGE).
- Hands-on experience in data preprocessing, feature engineering, and model assessment within the domain of graph-based machine learning specifically oriented towards detecting fraudulent activities.