Staff Data Scientist, LTV at Root

Job description

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. The Opportunity 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 - $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 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 bu