Bioinformatics Scientist – Mortality Risk Modeling

About Ethos

Ethos was built to make it faster and easier to get life insurance for the next million families. Our approach blends industry expertise, technology, and the human touch to find you the right policy to protect your loved ones.

We leverage deep technology and data science to streamline the life insurance process, making it more accessible and convenient. Using predictive analytics, we are able to transform a traditionally multi-week process into a modern digital experience for our users that can take just minutes! We’ve issued billions in coverage each month and eliminated the traditional barriers, ushering the industry into the modern age. Our full-stack technology platform is the backbone of family financial health.

We make getting life insurance easier, faster and better for everyone.

Our investors include General Catalyst, Sequoia Capital, Accel Partners, Google Ventures, SoftBank, and the investment vehicles of Jay-Z, Kevin Durant, Robert Downey Jr and others. This year, we were named on CB Insights' Global Insurtech 50 list and BuiltIn's Top 100 Midsize Companies in San Francisco. We are scaling quickly and looking for passionate people to protect the next million families!

About the Role

Ethos is looking for a bioinformatics scientist to join our actuarial and underwriting innovation team. In this role, you will help redefine how life insurers assess mortality risk by applying bioinformatics, survival analysis, and machine learning to novel health and behavioral data. You’ll lead research and model development efforts that go beyond traditional actuarial inputs — integrating signals from clinical biomarkers, medical history, and emerging data sources to create next-generation predictive frameworks.

This is a unique opportunity to bring your background in bioinformatics, computational health, or applied statistics into a space that’s ripe for reinvention. You’ll collaborate closely with actuaries, underwriters, data scientists, and engineers to design interpretable, production-ready models that improve both underwriting fairness and accuracy. Ultimately, helping us expand affordable life insurance access to more people.

If you're excited about applying scientific rigor to complex real-world risk problems, and want to help modernize an industry from the inside out, we’d love to hear from you.

Duties and Responsibilities

• Design and execute studies using novel data to improve mortality risk prediction.
• Develop and validate ML and statistical models for mortality scoring.
• Partner with Data Science and Engineering to bring models into production.
• Collaborate with Actuarial and Underwriting teams to align on validation and impact.
• Contribute to the methodology and roadmap of a modern, scalable mortality modeling system.

Qualifications and Skills

• You have experience working with medical ontologies, terminologies, or clinical data models that support robust analytical workflows. (e.g., ICD, SNOMED, LOINC, OMOP CDM, etc.)
• You’ve applied bioinformatics or statistical modeling techniques to real-world health, biometric, or survival datasets and know how to balance rigor with practicality.
• You’re fluent in tools like Python or R, and have experience with machine learning libraries or survival analysis frameworks.
• You thrive in cross-functional settings, working alongside actuaries, underwriters, engineers, and product teams and can communicate technical findings clearly to diverse audiences.
• You're excited to bring a scientific mindset to an industry ripe for reinvention and are ready to challenge assumptions while building credible, production-ready tools.
• MS or PhD in Bioinformatics, Statistics, Biostatistics, Applied Mathematics, Data Science, or similar.
• 4+ years of experience in applied statistical modeling or machine learning.
• Strong proficiency in Python or R and relevant libraries.
• Experience in survival modeling, biomarker analysis, or health risk scoring preferred.
• Excellent communication