Machine Learning Engineer, Jumio

• 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
• Friendly and supportive
• Adaptable and flexible
• Articulate and persuasive
• High IQ and EQ
• Curious and coachable
• Commercially Aware
• Resilient and tenacious
• Big picture and the detail

Desirable

• We are looking for a graph machine learning engineer
• In this role, you will get to work alongside various experts in product and engineering
• 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