Описание
Accountabilities:
- Design and develop machine learning infrastructure: Build and maintain scalable systems that support advanced machine learning algorithms, including supervised learning, unsupervised learning, and deep learning, to identify suspicious patterns and fraudulent behavior.
- Develop distributed real-time systems: Design, implement, and optimize distributed architectures that enable real-time data processing and fraud detection across high-volume datasets.
- Build large-scale data pipelines: Leverage big data technologies such as Apache Spark, Hadoop, HBase, and Cassandra to develop efficient machine learning pipelines and process data from large consumer-facing platforms.
- Implement streaming solutions: Develop and maintain real-time data processing systems using technologies such as Apache Kafka and Apache Flink to support rapid detection and analysis of emerging fraud patterns.
- Improve detection capabilities: Collaborate with machine learning specialists to enhance detection algorithms, optimize data processing workflows, and support the continuous improvement of fraud detection performance.
- Build engineering tools and validation frameworks: Develop internal systems, tools, and testing strategies that support new features, validate system behavior, and maintain software quality.
- Optimize system performance: Identify bottlenecks and improve the scalability, reliability, efficiency, and responsiveness of distributed services and data infrastructure.
- Co
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