Lead Machine Learning Engineer I, Lifetime Value

Details

• 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.
• BS in Computer Science, 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 Computer Science, 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.