Lead Machine Learning Engineer, Lifetime Value

Root Inc. is an insurance company using data and technology to develop smarter, more equitable insurance solutions. The Lead Machine Learning Engineer will build and operate scalable systems, infrastructure, tooling, and workflows supporting customer lifetime value modeling and other statistical machine learning applications. The role partners with data scientists, engineers, and business teams to productionize models, improve reliability, and enable data-driven decision-making.

Responsibilities:

• Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support
• Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision-making across the business
• Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling
• Improve the ML development experience by building better operational patterns and advancing production-ready ML practices
• Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production
• Collaborate with technical and business partners to solve high-value problems and improve the reliability and scalability of ML systems
• Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations

Requirements:

• BS in Statistics, Mathematics, Engineering, or a related quantitative field
• 5+ years of experience designing, building, deploying, and maintaining machine learning systems and ML model pipelines in partnership with data scientists
• Strong Python and software engineering fundamentals, with the ability to build maintainable ML systems and production-quality code
• Experience building and operating production ML systems, including deployment, monitoring, debugging, and workflow orchestration
• Ability to design reproducible systems with clear lineage, versioning, and operational visibility across complex ML workflows
• Comfort working in ML systems with interconnected components, simulation-driven logic, and embedded business rules
• Strong judgment around model evaluation, code quality, system reliability, and maintainable engineering tradeoffs
• Experience with cloud-based ML infrastructure and data platforms such as AWS, GCP, or Azure
• Experience with infrastructure as code, such as Terraform
• Clear communication skills and the ability to explain technical tradeoffs to both technical and non-technical audiences
• MS or PhD in Statistics, Mathematics, Engineering, or a related quantitative field
• Familiarity with customer lifetime value forecasting, simulation workflows, or Forecast vs. Actual analysis
• Experience with insurance or regulated financial products
• Exposure to ML and data tooling, orchestrators, and platforms such as MLflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark
• Experience building shared ML infrastructure, developer tooling, or reusable systems that improve data science productivity