General Motors - Austin, TX
posted about 2 months ago
The Staff Machine Learning Engineer will play a pivotal role in driving the vision and execution of LiftIQ, a Marketing Experimentation and Optimization Platform at General Motors. This position requires a technical leader with expertise in building scalable Machine Learning data products that facilitate experimentation and optimization. The engineer will be responsible for designing and implementing statistical and machine learning models that leverage both first-party and third-party data sources. Collaboration with cross-functional teams, including engineering, data science, and UX/UI design, is essential to refine the platform that enables effective experimentation, measurement, and optimization. In this role, the Staff ML Engineer will work closely with stakeholders from GM's various vehicle brands, subscriptions, and customer care products, as well as the Performance Driven Marketing team. The goal is to ensure business acceptance of models, metrics, and visualizations that demonstrate experiment performance and optimization. The engineer will develop platforms, tools, and capabilities that empower stakeholders and data scientists to identify marketing initiatives with high return on investment. The focus is on delivering consumer-centric, personalized solutions that will help GM navigate its transition to electric vehicles (EVs). The Staff ML Engineer will also be responsible for raising the standards of machine learning engineering within the organization. This includes improving best practices, producing exemplary code, documentation, and automated tests, as well as ensuring thorough monitoring of systems. The engineer will need to possess contextual business knowledge and functional domain expertise in experimentation systems related to marketing, media, customer interactions, digital channels, and loyalty and subscription services. A strategic roadmap with executable outcomes will be developed to provide business value and impact, while strong stakeholder management skills will be necessary to prioritize requests and transform them into actionable data products and insights.