Root Inc.

Data Scientist

• Serve as a senior technical leader for the Lifetime Value team while remaining hands-on throughout exploratory analysis, model development, deployment, monitoring, and production support
• Lead complex initiatives across interconnected models predicting customer conversion, retention, future premium, and claim losses
• Frame ambiguous modeling problems, evaluate analytical approaches, and refine technical direction
• Analyze interactions among component models, diagnose underperformance, and prioritize enhancements based on business value
• Design and validate experiments and measurement frameworks with clear success criteria
• Assess model performance and business impact after launch
• Partner with the team manager on quarterly planning, sequencing, capacity, milestones, and dependencies
• Work with machine learning engineers and technology teams to support production deployment of models, simulations, and forecasting workflows
• Balance rigor, reliability, interpretability, and delivery speed
• Communicate recommendations, risks, and tradeoffs to technical partners, business leaders, and senior decision-makers
• Guide and coach other data scientists
• Develop reusable methods, tools, and standards that improve data science work across the Lifetime Value team and related Quantitative Science work

Staff Data Scientist, LTV

• Develop and maintain telematics pricing and underwriting models
• Define problems, develop datasets, engineer features, build statistical models, evaluate models, validate with stakeholders, support regulatory requirements, and monitor post-deployment performance
• Build and maintain reliable modeling datasets, data pipelines, and transformations
• Validate data quality and lineage
• Productionize models and troubleshoot pipeline and system issues
• Improve the reliability and maintainability of the Telematics data ecosystem
• Manage the full telematics model lifecycle from raw data ingestion through development, production deployment, monitoring, and operational support
• Partner with Data Science, Data Engineering, Software Engineering, Pricing, Actuarial, Product, and Compliance teams
• Introduce new statistical algorithms and modeling techniques; prototype, test, retest, and scale approaches
• Adopt and refine modeling best practices, documentation standards, and peer reviews
• Provide modeling support, documentation, and explainability to state regulators
• Ensure statistical models comply with state-specific regulatory requirements and constraints

Lead Data Scientist, Telematics

Actuary

• Develop, enhance, and use internal capital models to inform risk appetite, reinsurance decisions, and financial strategy
• Contribute to stress testing, ORSA scenario development, and economic capital modeling
• Help implement and refine ERM frameworks, risk registers, and related processes
• Analyze and support evaluation and placement of quota share, excess of loss, and catastrophe reinsurance structures
• Collaborate with brokers and internal teams to assess financial impact and capital benefits of reinsurance options
• Model reinsurance impacts on earnings volatility, surplus preservation, and RBC
• Conduct monthly reserving analyses and processes
• Analyze trends, variances, and reserve adequacy across lines of business and coverages
• Assist with regulatory filings and support the appointed actuary
• Collaborate with Data Science, Finance, Accounting, Underwriting, and Claims
• Present technical findings to technical and non-technical audiences
• Contribute to process automation, analytics, and actuarial tool development using R, Python, SQL, GenAI, and internal platforms

Principal, Corporate Actuarial

Product Manager

• Support the Senior Product Manager in driving and executing Root’s product vision
• Help evolve the Root platform into a world-leading insurance operating system
• Gain insight into clients’ business objectives and develop platform solutions that support them
• Empathise with business users, including sales and claims agents, supervisors, data ops managers, and financial managers
• Coordinate collaboration among product, engineering, design, and business units
• Drive feature rollouts with support from product management, marketing, and client services
• Specify features with functional, technical, and design scope for engineering
• Drive delivery of quarterly squad objectives and raise blockers early
• Ensure features are user-tested and feedback is returned to engineering
• Ensure client-facing features include help centre guides, rollout communications, and developer documentation
• Execute monthly rollouts bundling shipped features with communications, documentation, and client-facing materials
• Maintain a clear product vision and roadmap focused on business-user requirements
• Solicit and process stakeholder and user feedback into the roadmap and product process
• Collaborate with marketing to turn new features into blog articles, LinkedIn posts, and prospect-facing collateral

Junior Product Manager

Performance Marketing

• Lead the design and development of production ML systems powering performance marketing optimization across channels
• Architect ML solutions combining multiple models, optimization algorithms, and embedded business logic
• Accelerate research-to-production through scalable infrastructure, reusable tooling, and improved ML development practices
• Partner with data scientists to translate new models and optimization approaches into scalable production solutions
• Design and operate real-time ML capabilities that perform reliably under production latency constraints
• Build monitoring and observability for interconnected ML systems to enable rapid issue detection and diagnosis
• Establish technical standards for maintainable, reliable ML systems and improve team development and operations
• Mentor data scientists and analysts on ML systems and production engineering
• Own technical direction across the ML lifecycle, from architecture and implementation through deployment and operation
• Partner with data scientists, Engineering, and business stakeholders on production ML systems for performance marketing

Staff Data Scientist, LTV

Lead Data Scientist, Telematics

Principal, Corporate Actuarial

Junior Product Manager

Lead ML Engineer, Performance Marketing