AD & Actuarial Associate - EB Pricing Modeling

AD & Actuarial Associate - AC07BE

We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

The Hartford seeks an Actuarial Associate within Employee Benefits to join a team of Data Scientists developing statistical models & machine learning solutions to support actuarial pricing and reserving modeling within Employee Benefits.

As an Actuarial Associate on this team, you will contribute to the entire model lifecycle while partnering with cross-functional business and technical partners to understand business strategies and design, develop, implement, and evolve modeling solutions. We use the latest data technologies, machine learning methods, MLOps, and Agile delivery frameworks to build innovative and efficient solutions that maximize business value.

This cutting edge and forward focused organization presents the opportunity for collaboration, self-organization within the team, influencing decision-making, and visibility as we focus on continuous business data delivery.

Opportunities:

• Gain in-depth knowledge of Employee Benefits products, including Short- & Long-term Disability, Life, and optional coverage
• Become an expert in both basic and advanced actuarial modeling techniques, with an opportunity for innovation by participating in projects related to Predictive & Generative AI
• Strengthen communication skills by presenting modeling results to both technical and non-technical audiences
• Build upon technical and analytical skills by utilizing Python & SQL in a cloud-native Data Science environment in AWS and GCP.

• Create statistical models, algorithms, and machine learning techniques to achieve financial objectives, solve business problems, and identify long-term opportunities that improve customer journey
• Collaborate and partner with business stakeholders in a way that supports the vision and sustains a culture that treats analytics as a corporate asset
• Lead execution of modeling and machine learning projects that focus on internal team collaboration with Data Scientists, Data Engineers, and Product Owners
• Assist in identifying and assessing the value of new data sources and analytical techniques to ensure ongoing competitive advantage
• Contribute to successful implementation of strategies to achieve targeted business objectives
• Develop knowledge of The Hartford's formal and informal structures, business processes, and data sources in your area of expertise
• Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function
• Provide economic, qualitative, and statistical support to ensure accuracy of characteristics and metrics being applied to business decisions
• Learning best practices to guide the direction of our Data Science and Data Engineering workflows

• 3+ years of relevant experience recommended
• Progress toward ACAS/FCAS or ASA/FSA preferred but not required. We will consider filling this role as part of our actuarial student program for a qualified candidate meeting requirements for entry into the program.
• Proficiency in statistical modeling, inference, and building machine learning algorithms in Python preferred.
• Proficiency in SQL and navigating databases to extract relevant attributes
• Proficiency in Unix and Git
• Exposure to the end-to-end modeling lifecycle, from requirements gathering to monitoring and validation
• Exposure to building modeling solutions in cloud-native environments, such as Sagemaker, a plus
• Able to communicate effectively with both technical and non-technical teams
• Able to translate complex technical topics into business solutions and strategies as well as turn business requirements into a technical solution
• Experience with leading project execution and driving change to core business processes through the innovative use of quantitative techniques