• Build the evaluation and validation framework for all agent-driven clinical recommendations • Develop patient risk stratification models for adherence prediction, adverse event likelihood, dosage titration optimization, and churn/dropout risk • Implement and manage the predictive analytics pipeline on AWS • Design and build the RAG architecture that grounds agent responses in clinical protocols • Own model lifecycle management • Build explainability layers for clinical recommendations • Collaborate with the clinical team to translate clinical protocols and pharmacy domain knowledge into model features, training labels, and validation criteria • Establish model monitoring and alerting

• Optimize, standardize and implement data science and machine learning solutions at scale and in cloud-based environments (Azure) • Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices • Write well-designed, testable, efficient code • Work closely with data engineering to continuously improve the way we consume data and deploy models in production • Design and lead on monitoring, troubleshooting, debugging and incident management for our ML pipelines • Be a trusted advisor and evangelist to the team and stakeholders on various aspects of ML Ops, from scaling and throughput, to infrastructure and deployment strategies

• Join our team and help build state-of-the-art generative systems for video and audio synthesis, performance transfer, and visual translation. • Shape core infrastructure and set best practices. • Communicate with stakeholders and mentor junior engineers. • Deliver high-quality ML pipelines in production environments.

• Design, build and release real-time decisioning systems that learn from interaction feedback at scale. • Own the modeling logic from how we represent users and signals to how reward attribution closes the loop and improves the next interaction. • Move ideas from papers and notebook prototypes including online learning policies and counterfactual estimators to production code that runs behind real-time APIs. • Design and build resilient streaming/batch pipelines that feed user state, reward signals, and offline replay. • Run honest experiments, A/B test what you ship, design offline evaluation for what you cannot, and kill your own work. when it doesn’t outperform against the baseline. • Continuously monitor and improve the quality, latency, observability, and scalability of the systems. • Collaborate across platform and product teams to turn research-grade ideas into production-grade products. • Share context, mentor engineers, raise the technical bar of the team, and help set direction for how we do ML at scale.