Lead Machine Learning Engineer, Lifetime Value

Root Inc. is a company on a mission to improve insurance solutions through data and technology. They are seeking a Lead Machine Learning Engineer to build systems and workflows for customer lifetime value modeling, collaborating with various teams to enhance decision-making and operational reliability.

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