Lead Machine Learning Engineer, Lifetime Value

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and enhance the systems that drive customer lifetime value modeling, encompassing everything from development and deployment to monitoring and production support.

• Collaborate with data scientists to implement statistical models, simulations, and forecasting workflows that facilitate informed decision-making across the organization.

• Expedite the transition from research to production by establishing scalable infrastructure, dependable workflows, and reusable tools.

• Enhance the machine learning development experience by creating improved operational patterns and advancing practices suitable for production-ready ML.

• Create tools and services that assist stakeholders in assessing model performance, comprehending business impact, and trusting model outputs in a production environment.

• Work alongside technical and business partners to address high-value challenges and enhance the reliability and scalability of machine learning systems.

• Promote best practices through mentorship, documentation, and effective communication regarding technical decisions, trade-offs, and operational considerations.

⛳️ Requirements

• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.

• Over 5 years of experience in designing, building, deploying, and maintaining machine learning systems and ML model pipelines in collaboration with data scientists.

• Strong foundation in Python and software engineering principles, with the capability to construct maintainable machine learning systems and production-quality code.

• Experience in creating and managing production machine learning systems, including deployment, monitoring, debugging, and workflow orchestration.

• Capability to design reproducible systems with clear lineage, versioning, and operational visibility throughout complex ML workflows.

• Comfort with machine learning systems that feature interconnected components, simulation-driven logic, and embedded business rules.

• Strong judgment regarding model evaluation, code quality, system reliability, and sustainable engineering trade-offs.

• Familiarity with cloud-based machine learning infrastructure and data platforms like AWS, GCP, or Azure.

• Experience with infrastructure as code, such as Terraform.

• Excellent communication skills with the ability to articulate technical trade-offs to both technical and non-technical audiences.

🏝️ Benefits

• Eligible for Competitive Bonus & Equity Offering