Ledger Investing is a startup looking to transform insurance capital markets. Insurance securitization is already a $100 billion market, and the Ledger marketplace is designed to scale this to a $1+ trillion market. We are venture-backed and were part of the Winter 2017 class at Y Combinator.
Our company is built around the idea that we can make the insurance market more efficient for everyone – insurers, investors, and consumers. We do this by creating financial instruments where performance is directly linked to an insurer’s earned premium and incurred claims costs, and where all parties have closely aligned incentives. In this way, insurers get access to capital more cheaply than traditional markets, while investors get access to a new class of assets with an attractive rate of return that is completely uncorrelated with stock and bond markets. These financial instruments are designed to be tradable on the secondary market, allowing for a greater degree of liquidity. Ultimately, consumers will benefit from a more competitive insurance market.
About the Position:
Data scientists at Ledger are critical to the success of our company. Their primary role is to develop impartial estimates of risk/return profiles for insurance-linked securities, which can include the following challenges:
- Blending portfolio-specific and industry-wide data to optimize a model’s bias-variance tradeoff.
- Building stochastic estimates of insurance underwriting cash flows based on loss ratio forecasts and loss development patterns.
- Constructing scalable data pipelines to integrate data from a wide variety of data formats and lines of business, and interpreting, validating, and summarizing that data to support modeling and analysis.
- Dynamically updating asset valuations in real-time based on current policy and claims data.
- Accounting for material shifts in the composition of a portfolio over time.
If these problems sound fun and interesting, we'd love to have you on our team. We offer competitive compensation, including health care subsidies, flexible working hours, maternity/paternity leave, and unlimited vacation. Our entire team is 100% remote, so you have the flexibility to live wherever you choose.
Although the job title we’re hiring for is Data Scientist, a degree or certification in data science is neither necessary or sufficient for this role. We look for candidates with all of the following attributes:
- Extensive knowledge of statistics, machine learning, and data science.
- Some familiarity with both R and Python; deep experience with at least one of them.
- Ability to build, extend, and maintain production-grade modeling pipelines; familiarity with reproducible research techniques.
- Ability to integrate data sources from a wide variety of sources (CSV, XLSX, databases, flat files, JSON, etc).
- Familiarity with SQL and relational databases; experience with PostgreSQL is a plus.
- Demonstrated track record of applying and adapting statistical methods to solve complex real-world problems.
- Comfortable with version control systems, particularly Git/GitHub.
- Strong oral and written communication skills, including the ability to tailor messages based on intended audience (e.g., explaining a concept to a business-oriented external stakeholder vs. technical documentation of that concept for internal use).
The following attributes will help candidates stand out from the crowd:
- An MS or PhD in statistics or machine learning.
- Working knowledge of property & casualty insurance, particularly from an actuarial and/or underwriting perspective.
- Familiarity with Monte Carlo-based Bayesian modeling software – e.g., Stan, PyMC3, BUGS/JAGS, or Pyro.
- Extensive experience with hierarchical modeling, time-series/state-space methods, and/or distribution fitting.
Ledger Investing is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, religion, creed, national origin or ancestry, ethnicity, sex, gender (including gender nonconformity and status as a transgender or transsexual individual), sexual orientation, marital status, age, physical or mental disability, citizenship, past, current or prospective service in the uniformed services, predisposing genetic characteristic, domestic violence victim status, arrest records, or any other characteristic protected under applicable federal, state or local law.