CVC/CVCC Xây Dựng Mô Hình Rủi Ro Thanh Khoản - Hà Nội - TA107

Mô tả Công việc

• The job holder proposes, initiatives and manages multiple behavioral models together with business units in order to address problems.
• The behavioral models, including but not limited to, term deposit’s early withdrawal and rollover, loan’s repayment and prepayment, CASA’s stickiness, overdue repayment, credit line model.
• Build cutting-edge algorithms and work with machine learning tools to deliver advance analytic solutions.
• The job holder is also involved in data preparation and the development of data marts required for model development.

Yêu Cầu Công Việc

Professional qualification: 

• Graduate university or higher level on major Finance – Banking, Economics, Econometrics, Computer science and other related majors. 
• FRM or CFA certificate is plus.

      ​Experience: 

• Have at least 2 - 3 years of relevant experience in areas of data analysis, modeling, machine learning on large amount of data, implementing and deploying various statistical models.
• Have strong background on Mathematics, Econometrics and quantitative risk models.

      Skills: 

• Proficient in computer skills, especially SAS, Python or equivalent programming language, and SQL.
• Fluent user in English speaking, writing, reading and listening.
• Strong communication skills, team-working skills and problem solving skills

      Benefits:

• Competitive salary and bonus package
• Staff loan with special interest rates
• Traing courses based on the job, Training framework/Learning RoadMap for each position
• Insurance in accordance with Labor laws + VPBank Care insurance for all employees. (insurance covered for family members for entitled employees);
• Annual leave (varied based on job grade)
• Travel allowance
• A dynamic and friendly working environment, full of great opportunities to develop your career and abundant interesting activities to join (Sports competitions, talent contests, teambuilding…)
• Working time: from Monday to Friday & 2 Saturday mornings/month