About the role
About Ethos
Ethos (Nasdaq: $LIFE) is a leading life insurance technology company on a mission to protect families by democratizing access to life insurance and empowering agents at scale. With its robust three-sided technology platform, Ethos is transforming the life insurance experience for consumers, agents, and carriers alike. Ethos offers instant, accessible products and a seamless online process that requires no medical exams and just a few health questions; it eliminates traditional barriers, making it easier than ever for everyone to protect their families. Ethos is redefining how life insurance is bought, sold, and underwritten.
About the role
We're looking for a Staff Data Scientist to own the product modeling roadmap and contribute to marketing measurement. You'll design, build, and deploy production ML models, partnering closely with PMs to scope and prioritize. The right candidate defines what to model before anyone asks, stress-tests their own assumptions, and communicates uncertainty in terms stakeholders can act on.
Duties and Responsibilities
- Own the end-to-end lifecycle of ML models powering product experiences, coverage recommenders, user-to-channel routing, and agent assignment logic
- Engineer features, prototype experiments, and ship iterative model improvements to production
- Work with engineering and PMs to define how model scores are consumed in real-time product surfaces
Qualifications and Skills
- 5+ years in data science or ML at established firms or high-growth tech
- Strong command of supervised learning and recommendation systems; you've shipped production models, not just notebooks
- Proficiency in Python (pymc, scikit-learn, XGBoost/LightGBM) and SQL
- Hands-on Bayesian experience: prior elicitation, posterior inference, and experiment design
- Experience deploying production models integrated with consumer-facing products
- Strong communication skills; able to translate model behavior and uncertainty into stakeholder recommendations
- Bias toward ownership; you identify what needs to be built before anyone files a ticket
- Recommender systems or personalization at scale
- Causal inference: diff-in-diff, uplift modeling, instrumental variables
- Marketing mix modeling, media attribution, or incrementality measurement
- Experience in insurtech, fintech, or other regulated industries