My client is an established global direct insurer, with strong footprints across the APAC region. This position will bring advanced knowledge and expertise in modelling methodology, data set generation and transformation, and statistical programming. Candidate will be responsible for the development of machine learning models that will revolutionise the Pricing and Underwriting process. He/She will need to understand and analyse complex insurance risk factors and articulate results to the various stakeholders, including (but not limited) to underwriters, product managers, and actuarial.
SCOPE:
- Extract and manipulate data using Python, or other data management tools from internal and external data sources
- Understand and combine data from various sources to create analytic data sets. Develop a solid working knowledge of how current systems and data sources are populated and sourced
- Build predictive models and analytic solutions using GLM, GBM, trees, and other machine learning techniques
- Analyse data, draw conclusions, and develop solutions to help profitability or growth
- Collaborate with Underwriting, Actuarial, and Project Managers to define projects
REQUIREMENT
- Degree in Actuarial Studies or related fields such as Computer Science, Statistics, Mathematics, etc
- At least 5+ years of experience in non-life insurance pricing modelling
- Possess good capabilities in data engineering, data transformation, data analysis and data visualisation
- Experienced with Python. Proficiency in other programming languages and actuarial software, such as SAS, R, SQL, Emblem/Radar is advantageous
- Hands-on experience with machine learning algorithms (e.g. GLM, GBM, Random forest etc.). Exposure with MLOps will be a plus
- Detail-oriented, organised, and superior analytical and problem-solving skills
- Excellent communication and interpersonal skills, including the ability to communicate complex technical issues in an effective way
