Staff Data Scientist

Posted Yesterday
Be an Early Applicant
Hiring Remotely in US
Remote
127K-224K Annually
Senior level
Consumer Web • Fintech • Insurance
Ethos blends industry expertise and technology to provide accessible and affordable life insurance coverage.
The Role
Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.
Summary Generated by Built In
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
  • 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 https://www.ethoslife.com/careers/

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 @ethos.com or @getethos.com.
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.

Skills Required

  • 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
  • Experience shipping production machine learning models
  • Proficiency in Python, including PyMC, scikit-learn, XGBoost, or LightGBM
  • Proficiency 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
  • 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
  • dbt or data pipeline experience
  • Experience with MLflow, feature stores, or other machine learning infrastructure tooling
  • Ability to evaluate tradeoffs between heuristics and machine learning solutions
  • Ability to align model performance metrics with business objectives and combine multiple models into a coherent solution
  • Experience contributing through code review, documentation, and mentorship
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The Company
HQ: San Francisco, CA
687 Employees
Year Founded: 2016

What We Do

Ethos makes getting life insurance instant and easy. Our approach blends technology and industry expertise, and the human touch to deliver an incredible life insurance experience. Ethos has raised over $400 million of capital from Sequoia, Accel, Google Ventures, Goldman Sachs, Softbank and more. We are one of the largest U.S. life insurance issuers. We continue to scale quickly and are looking for passionate Ethosaurs to protect the next million families.

Why Work With Us

Ethos is powered by people from all kinds of backgrounds. We embrace a one-team, one-dream mentality, while encouraging Ethosaurs to speak their mind and act boldly, even in the absence of consensus. We believe in decentralized decision making - not decisions made by committee. Here you can have an impact and true ownership over your successes.

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