About the Team:

Data scientists at Ledger are critical to the success of the company. Our team specializes in modeling the behavior of casualty insurance risk portfolios. We analyze and predict the performance of every insurance program that seeks capital through us, and our analyses are critical in determining on what terms, if any, our investors are willing to offer capital for each program. In short, our math forms the ground truth that serves as the basis of negotiations for transactions with tens of millions of dollars at stake.

About the Position:

The questions we focus on have broad overlap with traditional topics within actuarial science: estimating loss development, forecasting portfolio performance, quantifying tail risk, and the like. Instead of using traditional actuarial methods, we rely instead on more modern statistical approaches, particularly from the fields of Bayesian statistics and machine learning. We are building a robust, scalable, and highly automated pipeline by which we can price ILS transactions at scale with minimal human intervention.

At the same time, we recognize the value in more traditional actuarial perspectives, as they are the fruit of decades of practical experience. We also still regularly deal with stakeholders who are immersed in a traditional perspective, and actuaries play a vital role in translating to and from the modern Bayesian approaches we use internally. The synthesis of actuarial domain knowledge with cutting-edge techniques is at the crux of what we do.

About You:

Successful candidates will have all of the following attributes:

• Extensive knowledge of statistics, machine learning, and data science.
• Several years of hands-on experience in pricing and/or reserving for property and/or casualty insurance lines.
• Demonstrated ability to apply new statistical or modeling techniques to actuarial problems (e.g., loss development, rate segmentation, trend estimation, etc)
• Familiarity with modern Bayesian modeling techniques.
• Extensive experience with Python, R, Julia or other open-source languages with strong numerical computing ecosystems
• Strong attention to detail and ability to reconcile conflicting or ambiguous sources of information.
• Ability to work independently and communicate ideas effectively.

The following attributes will help candidates stand out from the crowd:

• A track record of publications and/or presentations on actuarial modeling topics.
• Contributions to open-source data science or machine learning projects.
• An actuarial designation (ie., ACAS or FCAS).
• MS or PhD in statistics or a related field preferred.
• Familiarity with Bayesian modeling using Stan.
• Familiarity with version control, especially Git/GitHub.