• Competitive
  • Hong Kong
  • CDI, Plein-temps
  • Citi
  • 18 déc. 17

HK - GCB - Senior BigData Modeler

HK - GCB - Senior BigData Modeler

  • Primary Location: HK,Hong Kong,Hong Kong
  • Education: Master's Degree
  • Job Function: Operations Credit Ops
  • Schedule: Full-time
  • Shift: Day Job
  • Employee Status: Regular
  • Travel Time: Yes, 10 % of the Time
  • Job ID: 17080530


Description

• 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.

Qualifications

• Prefer Master / PhD in Mathematics / Statistics / Computer Science / Engineering
• 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