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.
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.
