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:

Our data science practice is deeply Bayesian. We are invested in quantifying and disclosing every source of uncertainty in our modeling – our investors care about our level of confidence in our results as much as the top-line results themselves. The domain we work in is rife with small, noisy data
sets, and Bayesian hierarchical modeling techniques are typically quite beneficial. Many of the problems we are most interested in have received relatively little attention in the academic literature, and we have a robust agenda of potential model enhancements that will improve the state of the art in the industry and improve our company's bottom line.

If these problems sound fun and interesting, we'd love to have you on our team.

About You:

Successful candidates will have all of the following attributes:

• Extensive knowledge of statistics, machine learning, and data science
• Demonstrated track record of applying and adapting Bayesian statistical methods to solve complex real-world problems.
• Familiarity with hierarchical modeling, time-series/state-space methods, and/or distribution fitting.
• Extensive experience with Python, R, Julia or other open-source languages with strong numerical computing ecosystems
• Experience with Stan or another open-source tool for estimating Bayesian models via HMC; willingness to learn Stan.
• Ability to work independently and communicate ideas effectively.

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

• A track record of publications and/or presentations on Bayesian modeling topics.
• MS or PhD in statistics or a related field 
• Familiarity with version control, especially Git/GitHub.
• Working knowledge of property & casualty insurance, particularly from an actuarial and/or underwriting perspective.