Staff Data Scientist

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. The company 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. The company 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

• dbt or data pipeline experience

• MLflow, feature stores, or other ML infrastructure tooling

• Gets in the weeds without losing the bigger picture

• Knows the tradeoffs between simple heuristics and ML solutions and picks the right tool for the job

• Aligns model performance metrics to business objectives and combines multiple models into a coherent solution

• Raises the team's data science bar through code review, documentation, and mentorship

#LI-Remote  #LI-TL1

The US national base salary range for this full-time position is $127,000 - $224,000. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Please note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.

You can find further details of our US benefits at [link for members]

Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. At Ethos we are dedicated to building a diverse, inclusive and authentic workplace.

We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the SF Fair Chance Ordinance, we will consider employment for qualified applicants with arrests and conviction records.

To learn more about what information we collect and how it may be used, please refer to our  California Candidate Privacy Notice .

Recruitment Notice: Please be aware of recruitment scams. All legitimate communication from our team will only come from email addresses ending in [link for members] or [link for members].

We will never ask for payment, banking details, or sensitive personal information during the hiring process. If you are contacted by someone claiming to represent us from a different email address, please treat it as fraudulent.