Head of Underwriting at Ecclesiastical Insurance, shaping strategy and driving performance in the Gloucester office. Leading technical excellence and managing key accounts to deliver profitable growth.
About the role
- Responsible for optimizing underwriting strategies in personal lines Property & Casualty. Analyzing complex data sets to inform business decisions on risk and drive growth.
Responsibilities
- This Decision Science role is integral to optimizing underwriting strategies within the personal lines Property & Casualty business.
- The role will be responsible for the end-to-end process of acquiring, synthesizing, and analyzing complex data sets.
- Insights will directly inform critical business decisions related to risk precision, risk sharing, risk mitigation, and risk acceptability, ultimately driving profitable growth and enhanced member protection.
- Leverages advanced business, analytical and technical knowledge to participate or lead discussions with cross functional teams to understand and collaborate highly complex business objectives and influence solution strategies.
- Applies advanced analytical techniques to solve business problems that are typically medium to large scale with significant impact to current and/or future business strategy.
- Applies innovative and scientific/quantitative analytical approaches to draw conclusions and make 'insight to action' recommendations to answer the business objective and drive the appropriate change.
- Translates recommendation into communication materials to effectively present to various levels of management.
- Incorporates visualization techniques to support the relevant points of the analysis and ease the understanding for less technical audiences.
- Identifies and gathers the relevant and quality data sources required to fully answer and address the problem for the recommended strategy through testing or exploratory data analysis (EDA).
- Integrates/transforms disparate data sources and determines the appropriate data hygiene techniques to apply.
- Thoroughly documents assumptions, methodology, validation and testing to facilitate peer reviews and compliance requirements.
- Understands and adopts emerging technology that can affect the application of scientific methodologies and/or quantitative analytical approaches to problem resolutions.
- Succinctly delivers analysis/findings in a manner that conveys understanding, influences various levels of management, garners support for recommendations, drives business decisions, and influences business strategy.
- Provides subject matter expertise in operationalizing recommendations.
- Remains informed on current data and analytics trends, (Ex: Cloud, Data Mining, Python, Neural Networks, Sensor data, IoT, Streaming/NRT data)
- Identifies opportunities to continue to learn in the data and analytics space, whether informal (E.g., Coursera, Udemy, Kaggle, Code Up, etc.) or formal (E.g. Certifications or advanced coursework).
Requirements
- Bachelor's degree in quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline; OR 4 years of related experience in statistics, mathematics or quantitative analytics (in addition to the minimum years of experience required) may be substituted in lieu of degree.
- 6 years of data & analytics experience OR a minimum of 4 years of data & analytics experience and up to 2 years of progressive functional business relevant experience within the respective industry of responsibility (i.e. P&C, Bank, Finance, Marketing etc.) for a total of 6 years combined experience OR advanced degree in quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline and 4 years of experience in data/analytics or functional business experience within the respective industry of responsibility (i.e. P&C, Bank, Finance, Marketing, etc.).
- Demonstrates advanced skills in mathematical and statistical techniques and approaches used to drive fact-based decision-making.
- Advanced knowledge of data analysis tools, data visualization, developing analysis queries and procedures in SQL, SAS, BI tools or other analysis software, and relevant industry data & methods and ability to connect external insights to business problems.
- Experience with new and emerging data sets, and incorporation (data wrangling, data munging) into new insights.
Benefits
- comprehensive medical, dental and vision plans
- 401(k)
- pension
- life insurance
- parental benefits
- adoption assistance
- paid time off program with paid holidays plus 16 paid volunteer hours
- various wellness programs
- career path planning and continuing education
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