Data Scientist
2 months ago
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.
- 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.
- 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.
- 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.
- 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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