Data Scientist

2 months ago

Bangkok, Thailand Chubb Full time
Job Description

Educational
  • Bachelor's degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
  • 5+ years of related practical experience, preferably in commercial insurance sector.
  • Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
  • Familiarity with insurance industry regulations, standards, and best practices.


Responsibility
  • Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
  • Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
  • Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
  • Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
  • Enhance loss cost models over time by incorporating new data sources, refining variables,
  • and exploring innovative modelling techniques.
  • Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
  • Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
  • Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.


Functional Competency
  • Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
  • Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
  • Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently
  • Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
  • Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.


Qualifications

Educational
  • Bachelor's degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
  • 5+ years of related practical experience, preferably in commercial insurance sector.
  • Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
  • Familiarity with insurance industry regulations, standards, and best practices.


Responsibility
  • Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
  • Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
  • Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
  • Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
  • Enhance loss cost models over time by incorporating new data sources, refining variables,
  • and exploring innovative modelling techniques.
  • Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
  • Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
  • Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.


Functional Competency
  • Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
  • Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
  • Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently
  • Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
  • Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.

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