Predictive Modelling/ Data Analyst

Predictive Modelling/ Data Analyst

Job Title: Predictive Modelling/ Data Analyst
Location: Sydney
Reference: 1853
Contact Name: John Killick
Contact Email:
Job Published: January 17, 2018 18:41

Job Description

Great opportunity to join a global insurer and be based in Sydney. This role provides statistical analysis and predictive modelling expertise and advice to the APAC actuarial team. Sitting within the Global Analytics Team, you will also have the opportunity to support the business globally.


This position will be responsible for identifying data source, creating variables, developing various statistical models and supporting model implementation within the business.  The appointee will be expected to understand and analyse complex insurance risk factors and articulate results to the various stakeholders including underwriters, product managers and actuarial. 


You will have the opportunity to learn advanced analytical techniques if you have the desire and capability.


Major Responsibilities:

  • Extract and manipulate data using SAS or other data management tools from internal and external data sources.
  • Understand and combine data from various sources to create analytics data sets
  • Build predictive models and analytics solutions using tools such as GLM, logistic regression and decision trees.
  • Analyse data, draw conclusions, and develop solutions to help underwriting profitability or growth.
  • Assist IT in implementing and testing models.
  • Create and maintain documentation associated with models.


Knowledge & Skills:

  • Bachelor degree (or higher) in Mathematics, Actuarial Science, Statistics, Finance or a related field.
  • 1 to 3 years in data analysis or related actuarial experience in a business environment.
  • Experience with SAS, Emblem or programming in general is preferred.
  • Strong analytical skills.
  • Strong organizational skills.
  • Effective written and oral communication skills.
  • Responsive to service needs and operational demands.



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