Advanced identity proofing, risk assessment and compliance solutions that help organizations accurately establish, maintain and reassert trust.
About the job
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 behaviour.
- 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:
The Machine Learning Engineer role has the following requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a 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.
Great to have Experience and Qualifications:
- Solid understanding of graph embedding techniques, graph neural networks, and their applications in solving real-world problems.
- PhD in Computer Science or a related field with a focus on graph-based machine learning.
- Familiarity with distributed computing frameworks (e.g., Apache Spark) for scalable graph processing.
- Experience working with large-scale graph datasets and optimizing performance for computational efficiency.
- Contributions to open-source projects related to graph-based machine learning or graph algorithms.
- Strong analytical and problem-solving skills with a keen eye for detail in optimizing algorithms for performance and scalability.
Key Characteristics and Attitudes:
In a recent global survey these attributes were valued by Jumios in all locations and functions – we firmly believe in hiring for attitude as well as skill.
- Friendly and supportive
- Adaptable and flexible
- Articulate and persuasive
- High IQ and EQ
- Curious and coachable
- Commercially Aware
- Resilient and tenacious
- The big picture and the detail
Jumio is a B2B technology company dedicated to eradicating online identity fraud, money laundering and other financial crimes to help make the internet safer. We leverage AI, biometrics, machine learning, liveness detection and automation to create solutions that are trusted by leading brands worldwide and respected by industry thought leaders.
Jumio is the leading provider of online identity verification, eKYC and AML solutions. With a global footprint, we’re expanding the team to meet strong client demand across a range of industries including Financial Services, Travel, Sharing Economy, Fintech, Gaming, and others.
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