Job description

  • Working within the Group Data Science CoE, you will leverage huge volumes of Structured and Unstructured data to solve real business problems across the OCBC Group to increase revenues and deliver a great customer experience, improved productivity and reduce risk.
  • You will work closely with business to understand their problem statements & apply Data Science and analytics techniques to develop productionised solutions to capture the opportunity and generate business value.
  • We believe that variety is good. You’ll get the chance to work on a wide range of Use Cases across the business covering areas such as Personalisation, Sales & Marketing, Conversational AI, Fraud Detection, Anti-Money Laundering and Fraud Detection.


  • Work with business leaders across OCBC Group to identify opportunities for leveraging big data and Data Science to drive value for our customers and business.
  • Partner with Data Engineering to source internal & external datasets for analysis
  • Develop and maintain machine learning/deep learning models to achieve the desire business outcomes – such as marketing recommendations, fraud detection or credit scoring.
  • Coordinate with different functional teams to deploy models and smart applications into Production.
  • Use MLOps processes and tools to monitor and refine Production model performance and accuracy.
  • Partner with the Data Platforms teams to continuously enhance the OCBC AI and Data Platforms to ensure infrastructure is always at the leading edge.


Specific Knowledge:

  • 3 years+ of experience manipulating data sets and building statistical models, ideally with a Master’s or PHD in Statistics, Mathematics, Computer Science or other quantitative field.
  • Strong programming experience, with solid understanding of software engineering design patterns and best practices.
  • Experience with big data technologies such as Hadoop, Impala and Hive. Familiarity with SQL-like languages. Exposure to NoSQL databases such as MongoDB and HBase is an advantage.
  • Knowledge of a variety of machine learning techniques (clustering, recommender systems, Natural Language Processing, Deep learning etc.) and their real-world applications.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.).
  • Experienced in the use of CI/CD and DevOps tools such as Jira, Jenkins, GIT/Bitbucket.
  • Ideally has track record of deploying Machine Learning solutions into Production at scale.

Communication & Soft Skills:

  • Creativity to see possibilities within the data & translate into compelling stories, decisions and actions for non-technical business users.
  • Strong communication skills and drive to deliver business benefits in the real-world.
  • Genuine drive to learn and master new technologies and techniques.

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