Lead Scientist, Pricing

• Lead the development of intelligent pricing systems using reinforcement learning, constrained optimization, and simulation-based approaches
• Apply a combination of mathematical optimization, dynamic programming, and advanced reinforcement learning algorithms (e.g., model-free, policy gradients, model-based RL) to develop adaptive pricing strategies at scale
• Incorporate price elasticity modeling, demand forecasts, and business constraints into the optimization framework
• Build and validate simulation environments to evaluate policies offline and ensure robustness before production deployment
• Collaborate with engineering to productionize and monitor models in cloud environments such as AWS SageMaker
• Collaborate with engineering to productionize optimization models via batch or real-time pricing APIs (e.g., using AWS SageMaker)
• Partner with product, pricing, and revenue strategy teams to define objectives and translate pricing insights into business outcomes
• Define and execute model performance measurement strategies, including causal inference and uplift modeling and A/B testing
• Present findings, experimental results, and strategic recommendations to senior leadership

• MS or PhD in Statistics, Econometrics, Computer Science, Operations Research, or a related quantitative field
• 7+ years of hands-on experience building data science solutions for pricing, decision-making, or control systems
• Demonstrated success applying reinforcement learning in production settings, ideally in pricing or dynamic decision environments
• Proficiency with ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit-learn, DARTS) and programming languages (Python, R, SQL)
• Experience with Python-based libraries for mathematical optimization and reinforcement learning, and familiarity with simulation environments for offline policy evaluation
• Familiarity with cloud platforms and MLOps tools (e.g., AWS SageMaker, MLflow) for scalable model development and deployment
• Strong communication and presentation skills, capable of conveying complex analytical concepts to non-technical stakeholders
• Experience designing model evaluation and impact measurement frameworks, including causal inference

Prior experience in the hospitality, travel, or revenue management domain is highly desirable