Actuarial and Data Science Model Validation

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Actuarial and Data Science Model Validation

Overview of the Position:

The Hartford’s Model Risk Management function seeks a Risk Manager that will join a talented and high-performing Model Risk Management team. The successful candidate will lead efforts for the review and validation of critical models across the enterprise. A variety of modeling methodologies are employed at The Hartford including Advanced Analytics, Predictive, and AI/ML models in Data Science and traditional actuarial and business function methods. Models are used throughout The Hartford for a variety of purposes, including product pricing, asset pricing and valuation, risk management, reporting, economic capital, accounting, and operational functions.

The Model Risk Management team manages model risk across The Hartford by validating critical models, implementing consistent policies and standards, and maintaining appropriate model oversight. As part of the team, this role will focus primarily on validating models throughout The Hartford and reporting results to key internal stakeholders. Through the validation process, additional responsibilities include educating on modeling best practices and spreading model risk awareness across the enterprise.

Responsibilities:

• Perform model validations of critical models used across The Hartford’s functional areas and lines of business to ensure models are performing effectively and efficiently
• Ensure model calculations are accurate and appropriate for intended use
• Assess key data inputs and assumptions for accuracy and appropriateness
• Review model outputs for accuracy and appropriate downstream usage
• Deliver effective challenge to key modeling elements such as inputs, calculations, outputs, conceptual soundness, monitoring & controls, documentation, etc.
• Identify findings and recommendations to mitigate model risk, compile clear, concise and well-structured model validation reports, perform in-depth assessments of model risk, perform governance accountabilities related to findings tracking, remediation testing, and validation
• Build strong working relationships with key partners across the enterprise, including Data Science, Property & Casualty, Group Benefits, HIMCO, and a number of Corporate functions
• Pro-actively stay informed of department initiatives, deliverables, and reporting
• Assist in improving The Hartford’s Model Risk Management function, by monitoring external environment, implementing best practices, recommending process improvements, and evolving standards/guidelines

Qualifications:

• Advanced degree in Statistics, Applied Mathematics, Actuarial Science, Computational Science, Quantitative Economics, Engineering, or similar applied analytical field
• 5+ years of P&C, Group, Life or related insurance product experience
• ACAS/ASA or FCAS/FSA designation and/or CFA preferred
• Familiarity with different programming languages/technology platforms including Python, R, SAS/SQL, Excel/VBA
• Practical experience with predictive modeling/data science techniques
• Demonstrated experience and consistency in meeting deadlines while adapting to shifting priorities
• Strong analytical, critical and investigative thinking skills
• Solid verbal communication, listening, influencing, and written communication skills
• Ability to act independently with little direction, proactive self-directed accountability
• Solution oriented creativity and innovative thinking

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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