Staff Data Scientist

Ethos Life

1d • $127,000 - $224,000 • Remote

About The Position

Ethos is a life insurance technology company that aims to democratize access to life insurance and empower agents. The company uses a technology platform to serve consumers, agents, and insurance carriers, and offers an online application process without medical exams.

Ethos is seeking a Staff Data Scientist to own the product modeling roadmap and contribute to marketing measurement. The role designs, builds, and deploys production machine learning models in close partnership with product managers, including defining and prioritizing modeling work, testing assumptions, and communicating uncertainty in actionable terms. The position owns the end-to-end model lifecycle for product experiences, coverage recommendations, user-to-channel routing, and agent assignment, and helps determine how model scores are used in real-time product experiences.

The role also contributes to the data science team's standards through code review, documentation, and mentorship. The salary range for this full-time position is $127,000–$224,000 in base salary across US locations; individual compensation depends on location and other job-related factors. Bonus, equity, and benefits are not included in the stated base salary range.

Requirements

• At least 5 years of experience in data science or machine learning at established firms or high-growth technology companies.
• Strong command of supervised learning and recommendation systems, including experience shipping production models.
• Proficiency in Python, including pymc, scikit-learn, and XGBoost or LightGBM, and in SQL.
• Hands-on Bayesian experience with prior elicitation, posterior inference, and experiment design.
• Experience deploying production models integrated with consumer-facing products.
• Strong communication skills and ability to translate model behavior and uncertainty into stakeholder recommendations.
• Ownership mindset and ability to identify needed work before it is formally requested.
• Ability to choose between simple heuristics and machine learning based on the tradeoffs and needs of the problem.
• Ability to work through detailed implementation while maintaining a broader perspective.

Nice To Haves

• Experience with recommender systems or personalization at scale.
• Experience with causal inference, including difference-in-differences, uplift modeling, or instrumental variables.
• Experience with marketing mix modeling, media attribution, or incrementality measurement.
• Experience in insurtech, fintech, or another regulated industry.
• Experience with dbt or data pipelines.
• Experience with MLflow, feature stores, or other machine learning infrastructure tooling.

Responsibilities

• Own the end-to-end lifecycle of machine learning 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.
• Partner with product managers to scope and prioritize modeling work.
• Work with engineering and product managers to define how model scores are consumed in real-time product surfaces.
• Contribute to marketing measurement.
• Communicate model behavior and uncertainty as actionable stakeholder recommendations.
• Align model performance metrics with business objectives and combine multiple models into a coherent solution.
• Raise the team's data science standards through code review, documentation, and mentorship.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed