Data Scientist, LTV

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.

• Serve as a senior technical leader for the Lifetime Value team while remaining hands‑on throughout the modeling lifecycle, from exploratory analysis and model development through deployment, monitoring, and production support.
• Lead complex initiatives across interconnected models that predict customer conversion, retention, future premium, and claim losses. These predictions guide substantial marketing investment and significant product decisions.
• Frame ambiguous modeling problems, evaluate analytical approaches, and refine the technical direction as new evidence emerges.
• Analyze interactions among component models, diagnose underperformance and prioritize enhancements based on their potential business value.
• Design and validate experiments and measurement frameworks with clear success criteria, and assess model performance and business impact after launch.
• Partner with the team manager on quarterly planning and sequencing, considering capacity, milestones, and dependencies while communicating risks and helping remove blockers.
• Work with machine learning engineers and technology teams to support the production deployment of models, simulations, and forecasting workflows while balancing rigor, reliability, interpretability, and delivery speed.
• Communicate recommendations, risks, and tradeoffs clearly to technical…