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Staff Data Scientist / ML Engineer, LTV at Rippling Recruiting, Remote (United States)

Staff Data Scientist / ML Engineer, LTV at Rippling Recruiting in Remote (United States). $171,400 to $257,000 a year. Apply with swipeandhired.

We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
Root is seeking a Staff Data Scientist to lead the ongoing development and evolution of our customer lifetime value ecosystem. This is primarily a data science role, though we welcome candidates whose recent experience is in either data science or machine learning engineering. As the team’s technical lead, you will set the technical direction for the ecosystem's interconnected models and supporting systems. You’ll make hands-on contributions to the team’s most complex data science work while providing technical direction for the engineering needed to deploy, operate, and improve it.
Lifetime value predictions drive millions of dollars in marketing investment, provide value estimates for key business partnerships, and guide insurance product decisions. You will shape the priorities and the long-term technical direction for this high value system.

Salary Range: $171,400 - $257,000 (Eligible for competitive bonus and equity offering)

Root is a “work where it works best” company. Meaning we will support you working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus, Ohio.

How You Will Make an Impact

Own the technical direction of LTV modeling. Frame ambiguous problems, weigh analytical options, make principled tradeoffs, and change course as new evidence emerges.
Improve the models that drive major decisions. Lead complex initiatives across interconnected conversion, retention, future-premium, and claim-loss models; understand how component models interact, diagnose underperformance, and prioritize improvements by business value.
Keep a complex codebase and production system healthy. Guide the design and evolution of the LTV codebase and its training, inference, and deployment workflows so they remain modular, tested, reliable, efficient, extensible, and readable as models and dependencies grow.
Stay hands-on across the lifecycle. Contribute directly to exploratory analysis, feature development, modeling, production Python, deployment, monitoring, and production support.
Measure what matters. Design experiments and validation frameworks with clear success criteria, then evaluate model performance and business impact after launch.
Help plan and deliver quarterly and long-term priorities. Partner with the team manager to sequence work around capacity, milestones, and dependencies; communicate risks early and help remove blockers.
Bridge modeling and engineering. Work across data science and machine learning engineering boundaries to ship reliable models, simulations, and forecasting workflows, and explain recommendations, risks, and tradeoffs to leaders across Marketing, Finance, Product, and Customer Experience.
Raise the bar for the team. Coach data scientists and machine learning engineers through code review, design feedback, and mentorship; build reusable tools and standards that improve work across Quantitative Science and strengthen an inclusive, collaborative team culture.

What You Will Need to Succeed

BS, MS, or PhD in Statistics, Computer Science, Economics, or a related quantitative field.
8+ years of experience delivering complex, high-impact data science and production ML work. Your background may be anchored in data science or machine learning engineering, but you bring considerable hands-on experience in both and deep expertise in developing models that drive business decisions.
Strong command of foundational data science principles, including statistical methods, predictive modeling, time-series forecasting, experimental design, measurement, and validation, with deep expertise in survival analysis, including time-to-event modeling, cure modeling, recurrent events, and censoring.
Deep expertise in Python and SQL, with strong software engineering discipline in Python, including writing modular, well-tested, maintainable Python with clear interfaces and type annotations, and experience maintaining and refactoring a large shared codebase over time.
Experience designing, deploying, and operating systems of interacting production models, such as ensembles or chained predictions, including shared training and inference pipelines, workflow orchestration, version control, CI/CD, monitoring, and production support.
Demonstrated ability to frame ambiguous modeling and systems problems, evaluate technical tradeoffs, estimate the value of the work, and prioritize high-leverage opportunities.
Strong communication and relationship-building skills, with the ability to connect technical work to business goals and explain decisions, risks, and tradeoffs to varied audiences.
A track record of shaping technical direction and raising the quality of others’ work through coaching, review, and shared standards while remaining accountable for hands-on delivery.

Nice to Have

Experience with customer lifetime value forecasting, simulation workflows, forecast-versus-actual analysis, or causal inference.
Experience with insurance or regulated financial products.
Experience with tools in or adjacent to Root’s ML stack, such as AWS, Docker, dbt, Airflow, Metaflow, or MLflow.
Experience turning prototypes of new methods or data science tools into durable improvements in how a team works.

As part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through.
Please see our Privacy Notice available HERE for more information on how we process your personal data.

Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root. The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process. If reasonable accommodation is needed, please contact [email protected].

Root was founded on the belief that car insurance is broken, and we set out to change it. We’re harnessing the power of technology to revolutionize this archaic, complicated industry. Using machine learning and mobile telematic platforms, we’ve built one of the most innovative insurtech companies in the world.