Position: Senior ML/DL/AI
Company: Fluvius
Experience required: 5y
Category: Data Engineer
English level: upper
Published: 2023-08-01T00:00:00+03:00

**Required Skills:**

- Proven experience (at least 3 years) working on fraud detection and/or outlier detection projects using Graph Convolutional Networks (GCN) and Graph Neural Network (GNN) models.
- Advanced degree (MSc/PhD) in Computer Science, Data Science, Machine Learning, AI, or a related field.
- Strong background in handling imbalanced datasets and designing solutions to address data class imbalance.
- Proficiency in programming languages such as Python and deep learning frameworks like TensorFlow or PyTorch.
- Experience in analyzing and interpreting complex learning event sequences for anomaly detection purposes.
- Exceptional ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Prior experience with insurance and/or healthcare data is a significant advantage.

**Responsibilities:**

- Advanced Model Development: Design, develop, and implement state-of-the-art Graph Convolutional Networks (GCN) and Graph Neural Network (GNN) models, etc. for fraud detection and outlier detection.
Leverage your extensive experience in handling imbalanced data to create robust models that effectively identify anomalies in complex datasets.
- Specialized Domain Expertise: Apply your deep knowledge of insurance and/or healthcare data to enhance the accuracy and relevance of the developed models. Understand the intricacies of the industry-specific data to tailor the model architecture and features accordingly.
- Learning Event Sequences: Utilize your expertise in handling learning event sequences to build models that can effectively capture and analyze sequential data patterns for fraud detection. Develop models that can uncover anomalies and irregularities within complex temporal data.
- Explainability of Anomalies: Develop methods to explain the detected anomalies and outliers to stakeholders, making complex results interpretable for non-technical audiences.
- Provide insights into the factors contributing to anomalies, aiding in decision-making processes.
- Collaboration: Collaborate with cross-functional teams including data engineers, domain experts, and software developers to integrate the developed models into our existing systems and applications. Contribute to a cohesive team environment that fosters knowledge sharing and innovation.
- Research and Innovation: Stay updated with the latest advancements in the field of machine learning, deep learning, and AI. Actively research and propose innovative approaches to improve the accuracy and efficiency of our fraud detection and outlier detection methods.

**Project description:**
Fraud Detection/Outlier Detection with Graph Convolutional Networks (GCN) and Graph Neural Network (GNN)

**What we offer:**

- Healthy workplace environment
- Strong and friendly development team
- Competitive reward
- Compensation for education
- Flexible schedule
- 15 paid vacation days, 7 paid days-off