• Design, prototype, and productize scalable machine learning solutions. • Collaborate with clients and teammates in a high-performing team environment. • Tackle open-ended problems and focus on the quality and impact of your work.

• Support ~25-30 Learners in virtual training programs • Provide synchronous and asynchronous support in German • Engage in mandatory Pre-Program TA Onboarding • Monitor Slack channels for Learner questions • Facilitate breakout rooms during lectures • Assist during technical difficulties • Grade Learner submissions and provide feedback • Organize Individual Office Hours via Zoom

• Define the roadmap for the internal ML platform and ML Ops, taking ownership of existing tools and selecting the right additions to meet business needs. • Lead a cross-functional team of ML Engineers and specialists to build robust CI/CD pipelines for machine learning. • Establish highly optimized automations for model development, ensuring consistency in feature stores, model versioning, and deployment strategies. • Optimize cloud infrastructure to handle massive datasets while maintaining a keen eye on compute costs and resource allocation. • Implement comprehensive monitoring for model drift, latency, and bias to ensure our models remain high-performing. • Act as the primary interface between Data Science teams, Emerging Technologies and Core Engineering to remove bottlenecks in production pipelines.

• Own ML systems & tooling in production • Define and evolve R2’s ML platform architecture • Architect, implement, and deploy ML/DL models into production environments • Design and build automated monitoring systems to track model performance • Develop frameworks to automate recurrent Data Science workflows • Partner with Product, Engineering, and Risk teams to align ML/DL solutions with business goals • Mentor junior and senior data scientists and analysts