Staff Data Scientist, LTV

The Opportunity 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. 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 I to lead the design, development, and oversight of the models that power our customer lifetime value ecosystem. This ecosystem includes hundreds of interdependent models and workflows covering conversion, retention, future premium, and claim losses. Its complexity and business importance require a deeply experienced data scientist who can guide and contribute to the team’s most challenging technical work while partnering directly with machine learning engineers on production deployment. Lifetime value predictions shape some of Root’s most consequential decisions, driving millions of dollars in marketing investment, informing valuations for key business partnerships, and guiding insurance product decisions. In this role, you will guide the technical work of the team’s data scientists and partner closely with machine learning engineers, data and software engineers, and business teams to improve decisions across Marketing, Finance, Product, and Customer Experience. You will also be a hands‑on individual contributor on that work. This role carries broad technical responsibility for the quality and evolution of lifetime value modeling. You will resolve complex modeling questions and dependencies, evaluate enhancement opportunities, make principled tradeoffs, and establish practical standards for experimentation, validation, and monitoring. In partnership with the team manager, you will help shape quarterly priorities and longer‑term technical direction. The ideal candidate combines deep modeling expertise and strong execution with the ability to improve the work of others. You can personally deliver complex analyses and models, exercise sound judgment across a highly complex ML system, and help develop other data scientists. Salary Range $171,400 - $214,200 (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 healthy. Guide the design and evolution of the LTV codebase so it remains modular, tested, efficient, extensible, and readable as models and dependencies grow. Stay hands‑on across the lifecycle. Contribute directly to exploratory analysis, feature development, modeling, 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. Partner across the business. Work with machine learning, data, and software engineers 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 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 work, including production 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 and operating systems of interacting production models, such as ensembles or chained predictions, with attention to computational efficiency, clarity, reproducible training and inference, workflow orchestration, version control, deployment, monitoring, and production support. Demonstrated ability to frame ambiguous modeling problems, evaluate technical tradeoffs, estimate the value of modeling 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. Familiarity with cloud ML tooling (e.g., AWS, Docker, dbt, Airflow, Metaflow, 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. 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