Lead Machine Learning Engineer

Our client is seeking a Lead Machine Learning Engineer I to build and scale the machine learning systems that power customer lifetime value modeling and business forecasting initiatives.

As a key member of the team, you will work alongside data scientists, engineers, and business stakeholders to develop scalable ML infrastructure, production-ready workflows, and reliable systems that support strategic decision-making across Marketing, Finance, Product, and Customer Experience.

• Build and enhance machine learning systems supporting customer lifetime value modeling, deployment, monitoring, and production operations.
• Partner with data scientists to product ionize statistical models, simulations, and forecasting workflows.
• Accelerate the transition from research to production through scalable infrastructure and reusable tooling.
• Improve the machine learning development experience through operational best practices and production-ready workflows.
• Develop tools and services that help stakeholders evaluate model performance and business impact.
• Collaborate with technical and business teams to solve complex, high-value problems.
• Mentor team members and promote engineering best practices through documentation and knowledge sharing.

• Bachelor's degree 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 pipelines.
• Strong Python programming and software engineering expertise.
• Experience deploying, monitoring, debugging, and operating production machine learning systems.
• Knowledge of reproducible ML systems, versioning, lineage tracking, and workflow orchestration.
• Experience working with cloud platforms such as AWS, GCP, or Azure.
• Experience with Infrastructure as Code tools such as Terraform.
• Strong communication skills and the ability to explain technical concepts to both technical and non-technical audiences.

• Master's or PhD in a related quantitative field.
• Experience with customer lifetime value forecasting, simulation modeling, or forecast-versus-actual analysis.
• Experience with in insurance or regulated financial services environments.
• Familiarity with ML and data platforms including MLflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark.
• Experience building shared ML infrastructure, developer tooling, or systems that improve data science productivity.

If you're passionate about building scalable machine learning systems that drive meaningful business outcomes, we'd love to connect.