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Work with regional and global Internal Fraud BigData analytics team, independently manage and deliver fraud analytics project in Hadoop ecosystem across a big variety of large datasets, understand fraud / risk elements and pattern, and design corresponding analytical solution.
Lead the research and design of machine learning / deep learning models on banking transactional data, staff behavioral data, and more importantly, on Natural Language Process and Image Processing domain to deliver state-of-the-art surveillance solution to the franchise.
Be partner with technology function in Citi for data management including new data acquisition, data model design, and data quality investigation in Hadoop ecosystem.
Lead the UAT testing, MIS reporting and documenting the solution designed to meet the risk and process control standard of Citi.
Be responsible for knowledge transferring, coaching on advanced analytics and generalize the solution to all the regional internal fraud teams within Citi via liaising with corresponding stakeholders across the globe.
5+ years' working/research experience in data mining / machine learning, Experience on Fraud/Anomaly Detection / Risk Management is a plus
Proficient in data query language: SQL, Hive. Proficient in using Python and C++ to conduct data mining, modeling;
Deep knowledge and project experience using basic data mining techniques - principle component analysis, factor analysis, hypothesis testing, clustering etc., and Text Mining techniques - tokenization, lemmatization, parsing, semantic analysis;
Knowledge and project experience on Natural Language Processing/Image Processing/Audio Processing
Knowledge and project experience on supervised machine learning models - Regression, Logistic, Neural Network, SVM, Bayesian Network; Un-supervised machine learning models - Nearest neighbor, K-means. Knowledge on Deep Learning is a plus
Proficient in both spoken and written English. Fluency in other languages is a plus
Focused and proactive; Open-minded and creative; Matured and teamwork
Be ready for and willing to travel or short-term oversea assignment