• Develop and deploy end-to-end ML pipelines using modern MLOps practices, cloud-native platforms (e.g., AWS Sagemaker), and scalable infrastructure. • Conduct causal analysis and treatment effect estimation using DML, causal forests, uplift modeling, and other counterfactual inference techniques to guide high-stakes business strategy. • Build, train, and optimize predictive and prescriptive models for use cases like pricing, promotions, inventory, marketing attribution, and personalization. • Integrate models into production systems and monitor their performance using advanced observability tools (yes, even Happyface), including diagnosing drift and data quality issues. • Partner directly with business leaders to translate ambiguous business problems into machine learning frameworks that deliver measurable ROI. • Collaborate with engineering teams to improve data pipelines, ensure model reproducibility, and maintain version-controlled, CI/CD-enabled ML workflows. • Continuously research and apply emerging techniques in AI, including generative AI, automated feature engineering, and reinforcement learning. • Take complex, high-impact problems end to end - from exploration and feature design through model selection, backtesting, and production deployment with clear impact metrics. • Design robust experiment and quasi-experiment setups (A/B tests, holdouts, staggered rollouts) and recommend approaches when fully randomized tests are not feasible.