Staff Data Scientist
Own the product modeling roadmap and contribute to marketing measurement as a Staff Data Scientist at Ethos Life.
Posted by employer 2 days ago
First seen on Joblaze 2 days ago
Last verified on the company career page 12 hours ago
Skills & Technologies
What you'll build
- Own the end-to-end lifecycle of ML models
- Engineer features and prototype experiments
- Ship iterative model improvements to production
- Work with engineering and PMs to define model score consumption
Must have
- 5+ years in data science or ML
- Strong command of supervised learning and recommendation systems
- Proficiency in Python and SQL
- Hands-on Bayesian experience
- Experience deploying production models
Nice to have
- Recommender systems or personalization at scale
- Causal inference
- Marketing mix modeling
- Experience in insurtech or fintech
- dbt or data pipeline experience
Requirements
- Experience
- 5+ years
Not disclosed in this posting: visa sponsorship.
Joblaze summary
The Staff Data Scientist at Ethos Life is responsible for managing the entire lifecycle of machine learning models that enhance product features and improve user experiences. This role requires expertise in supervised learning, recommendation systems, and strong proficiency in Python and SQL, along with hands-on experience in deploying models in consumer-facing applications. Ideal candidates have over five years in data science, preferably in insurtech or fintech, and possess strong communication skills to convey complex model behaviors to stakeholders. Ethos Life emphasizes a collaborative environment where data-driven insights directly influence product development.
Joblaze insights
- Listed 2 days ago — first seen on Joblaze November 4, 2026. Last confirmed on Ethos Life's careers page November 6, 2026.
- Salary band is below the typical range for Data Science roles (median ~$178,250).
- Starts above 7% of 29 comparable staff data science roles in United States that list Python we track (median $217,000 across 15 companies). See Python salary trends
- Python appears in 73.9% of 46 comparable staff data science roles in United States; dbt appears in 10.9% of 46 comparable staff data science roles in United States.
Quick facts
- Is the Staff Data Scientist role remote?
- Yes — Ethos Life lists this as a fully remote position.
- What's the salary range?
- Ethos Life lists $127,000–$224,000 for this role.
- How much experience is required?
- At least 5 years of relevant experience for this Staff Data Scientist role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Bayesian, LightGBM, MLflow, Python, SQL, XGBoost.
- What seniority level is this role?
- Ethos Life targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Staff Data Scientist role at Ethos Life.
From the original posting
About Ethos
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.
Standard company text repeated across Ethos Life's postings is omitted here.