MGM Resorts InternationalPrincipal Data Scientist of Commercial Analytics & Data Science MGM Resorts InternationalPrincipal Data Scientist of Commercial Analytics & Data ScienceNVTHE DAY-TO-DAY: Lead multiple large, complex data science initiatives with minimal supervision Design, develop, and deploy advanced AI/ML models supporting personalization and commercial analytics Perform hands-on data science work including data collection, mining, normalization, and feature engineering Develop predictive and statistical models, including hyper-parameter tuning and optimization Deploy, scale, and monitor machine learning models in production environments Partner closely with Engineering and Analytics teams to define data requirements and improve data pipelines Maintain and enhance CI/CD frameworks for data science and model orchestration Conduct A/B testing and test-and-learn experiments to optimize key business KPIs Evaluate model quality, monitor performance, and continuously improve outcomes Review peer code, write unit tests, and provide technical feedback on modeling and pipeline approaches Translate complex analytical insights into clear, audience-appropriate presentations for business and executive stakeholders Collaborate with Marketing, Product, Creative, Strategy, and Customer Engagement teams to drive measurable impact Support the interview process and technical evaluation of Data Scientist candidates. THE IDEAL CANDIDATE: Bachelor's degree or higher required 8+ years of prior relevant experience Expert-level proficiency in Python, SQL, and modern ML frameworks (e.g., Spark, TensorFlow, Keras) Proven ability to deliver multiple complex projects simultaneously in cross-functional environments Strong problem-solving skills with a focus on root-cause analysis and practical solutions Excellent communication and collaboration skills, including presenting to non-technical audiences Experience building MVPs under tight timelines while maintaining high quality Background in hospitality, retail, or customer-centric industries preferred.