Lead Machine Learning Engineer, Lifetime Value

🕒 vor 2 Monaten

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Lebenslauf hochladen

Root Inc.

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

Gegründet 2015

🚘 Automobilindustrie

💼 Beratung

🛡️ Versicherung

💰 €200.000.000 Post IPO debt im 2024-11

Automotive • Consulting • Insurance

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Beschreibung

• 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.

🎯 Anforderungen

• 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.

🏖️ Vorteile

• Eligible for Competitive Bonus & Equity Offering