Leading data architecture and executing AI strategy for digital asset integration at QCP. Building a high - performing data team and ensuring compliance and operational excellence.
About the role
- Director leading Workers’ Compensation Pricing Models within Actuarial group, driving innovation in predictive modeling, machine learning, and actuarial analytics for significant business line.
Responsibilities
- Lead WC Modeling: Serve as the model owner for Workers’ Compensation predictive models, ensuring alignment with pricing and business priorities.
- Partner on strategic vision while managing team capacity and driving timely model deliverables.
- Model Development & Deployment: Lead design, maintenance, and deployment of predictive models for frequency, severity, and loss cost estimates, ensuring production readiness and effective integration into pricing workflows.
- Cross-Functional Collaboration: Partner with Actuarial, Data Engineering, Underwriting, and Product teams to connect modeling initiatives with pricing strategies and organizational goals, promoting shared problem-solving.
- Innovation: Drive modernization through advanced modeling techniques, machine learning, and AI to enhance efficiency and decision-making.
- Lead experimentation with new data sources, features, and methodologies to uncover insights and advance strategic objectives.
- Operational Excellence: Ensure robust documentation, validation, and governance, along with scalable processes to monitor model performance and maintain reliability.
- Talent Development: Mentor and develop team members, fostering a culture of curiosity, ownership, and continuous learning.
- Support hiring and onboarding, including intern and actuarial student rotations.
- Strategic Influence: Participate in enterprise initiatives to ensure Workers' Compensation predictive models are efficiently integrated into core data and system platforms.
Requirements
- 8+ years of relevant analytical experience
- Master’s or Ph.D. in Statistics, Applied Mathematics, Quantitative Economics, Actuarial Science, Data Science, Computer Science, or a similar analytical field, or a relevant professional designation (e.g. FCAS, FSA, CSPA, ACAS, ASA)
- Expertise in statistical modeling, inference, and building machine learning algorithms in Python and/or R
- Expertise in the end-to-end modeling lifecycle, from requirements gathering to monitoring and validation.
- Experience in SQL and familiarity with cloud-native environments (e.g., Snowflake, Sagemaker).
- Able to communicate effectively with both technical and non-technical audiences.
- Able to translate complex technical topics into business solutions and strategies as well as turn business requirements into a technical solution.
- Prior Management Experience Highly Preferred
Benefits
- Short-term or annual bonuses
- Long-term incentives
- On-the-spot recognition
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