Machine Learning Engineer

Summary

Build ML models for financial crime prevention using graph learning and time-series analysis in Python and PyTorch.

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Conduct data analysis, feature engineering, and feature extraction for data within the financial industry
Develop and implement cutting-edge machine learning algorithms focused on graph learning and time series analysis for applications such as transaction monitoring, anti-money laundering, and cryptocurrency analysis
Maintain and optimize data pipelines, enhancing existing solutions through pre- and post-processing improvements, fine-tuning, performance evaluation, visualization, and testing
Collaborate with cross-functional teams to identify and address customer needs and aspirations
Proactively resolve ambiguity and tackle technical issues, driving innovation and efficiency in processes

Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Statistics, or related fields
Proficiency in at least one machine learning development framework, such as PyTorch, Keras, or TensorFlow, with hands-on experience in environment control
Experience with programming, monitoring, visualization and project collaboration tools, including Python, VSCode, Conda, Git, and MySQL
Knowledge of statistical machine learning and deep learning, particularly the models for graph and/or time series data, such as knowledge graph, spatial-temporal graph, and multivariate time series
Interest in areas such as anomaly detection, federated learning, transfer learning, self-supervised learning, or uncertainty quantification
Knowledge in LLM is a plus
A passion for coding, programming, innovation, and problem-solving
A keen interest in anti-money laundering practices and regulatory compliance
Proficient in written and spoken Chinese (Cantonese or Mandarin); fluency in English is a plus