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 quality of our analyses is fundamentally dependent on the quality of the underlying data. We regularly receive policy and claims data from a wide variety of sources and in a wide variety of formats. In order to provide accurate and up-to-date projections of portfolio performance to investors, we need accurate and up-to-date data on the insurance programs in the portfolio. We have tools in place to automate the process of loading of updated data in a known format, but we still need keen human eyes whenever we get raw data in a new file format.

Our data analysis team ensures that we correctly understand the semantics of each new source file format, and that new data sources are correctly uploaded to our risk data warehouse. Source data is often ambiguous, incomplete or inconsistent, and the team works directly with risk originators to identify and fix these issues. The team helps other teams within the company construct business definitions and build queries for metrics of interest that can be derived from policy and claims data. Finally, the team works closely with engineers to improve the architecture and performance of the data warehouse.

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:

• Professional, hands-on experience manipulating policy- and claim-level data from property and/or casualty insurers.
• Strong understanding of the core business concepts in property and casualty insurance.
• Demonstrated ability to identify, isolate, and correct issues in messy data.
• Ability to work with and integrate data sources in a wide variety of formats (CSV, XLSX, databases, flat files, JSON, etc) and perform checks for integrity, consistency, and accuracy against known sources of truth.
• Significant experience with Python, R, VBA, or another language commonly used for data manipulation; basic exposure to Python 
• Ability to work independently and communicate ideas effectively.

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

• Experience with writing unit tests and documentation for source code.
• Familiarity with core concepts in statistics and data science.
• Familiarity with version control, especially Git/GitHub.
• Experience with data visualization, particularly for data dashboards .