Data Scientist

Location: Bangkok, Thailand

Rank: All levels

Job Description

From smartphones to artificial intelligence, digital channels continue to drive how customers experience their brand. KPMG Thailand aims to help clients rethink their strategies and their business models so that they can embrace the latest digital thinking. This in turn will help them be more competitive and efficient.

KPMG technology and data team is helping banks, insurer and non-bank client on their digital transformation journey. The team is involved in a comprehensive range of consulting services and solutions to our client focusing on data and technology domain as well as help client solving their strategic problems focused on improving performance and profitability.


  • Actively seek out opportunities to innovate by using non-traditional data and new modelling techniques fit for purpose to the needs of our clients
  • Enhance existing analytic techniques by promoting new methodology and best practices in analytics field
  • High interaction with external clients, and manage internal and external stakeholders
  • Create and monitor dashboards (Power BI and Tableau) and reports for relevant projects.
  • Define detailed scope and methodology, creating and executing on the framework with appropriate data mining techniques
  • Building data science visualization capabilities to solve client's problems
  • Drive innovation through using data science techniques
  • Act as data science advocate within our client, advising and coaching analytical teams and sharing best practices and case studies.
  • Continually look at the environment to challenge our assumptions around new sources of data, potential analytics partners, tools, talent and infrastructure.
  • Explore leading methodologies and best practices to other teams and importing successful methodologies from other international markets


We are looking for a motivated, analytical minded individual with a track record of using data science and analytics expertise to unlock business value. A successful candidate should have accumulated a variety of industry experience and have solid background in mathematic / statistic.

  • Degree in Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, or engineering or equivalent
  • Experience in performing data exploration and feature experience engineering
  • Experience in Credit Risk Analytics and Digital Marketing Analytic would be an added benefit
  • Deep analytical expertise in applying statistical solutions to business problems
  • Proficiency with modelling software, experience with Python, R, SAS, Matlab,or similar instruments
  • Practical experience in building and applying machine learning models (regression, clustering, classification: gradient boosting, random forests, linear models, deep learning etc.), understanding in how these algorithms work and end-to-end development skills from business understanding and data preparation to quality assurance of ML models
  • Demonstrated ability to innovate solutions and solve business problems capabilities
  • Excellent presentation skills, including strong oral and written
  • Self-motivated, results-oriented individual with the ability to progress multiple priorities concurrently