Our client is a multinational bank with an all-rounded financial operations at its prime within the APAC region.  They are looking for a Data Scientist to lead a team in developing machine learning models and data-driven strategies

Responsibilities: 

• Work closely with senior leadership across different business units to identify business needs and challenges that can be addressed through data-driven advanced analytics
• Design and implement customized machine learning models and data-driven solutions using SAS, Python, or R
• Responsible for experimental test design and ensure an robust A/B test framework to accelerate learning
• Interpret & translate analytic output into actionable insights
• Work with other analytics team to design, develop, and implement dynamic visualization tools aiming to provide actionable insights for better business decision and management
• Leverage both internal and external data to create traditional and machine learning statistical models aiming to improve customer engagement and business performance
• Work with other business teams and stakeholders to better understand customers’ behavior in digital channels and help creating go to market strategies leveraging ad-hoc analytics
  

Requirements:

• University degree in Statistics, Computer Science, Mathematics, Engineering or another quantitative discipline
  
• Minimum 8 years’ working experiences in quantitative analytics, business analytics, modeling, and machine learning.  Managerial experiences (onshore and/or offshore) is a plus 
• Solid experience of application of modeling / analytics in the context of the customer life cycle management across multiple distribution channels with effectiveness measurement and test learning
• Professional experiences with R/Python (SciKit-Learn, Pandas, TensorFlow, Keras etc) or  / SAS (Base, SAS STAT, Enterprise Miner etc)
• Proficient in visualization tool such as Qlikview, PowerBI is a plus
• Advanced knowledge in SQL or Hadoop is a plus
• Experiences in clustering, supervised/unsupervised learning, logistic regression, SVM, classification algorithms, and other data science methods