About the Company

Duetto, the industry-leading hospitality revenue management system, leads the way in helping hotels, resorts and casinos optimize revenue and boost profit. Our leading SaaS platform, expanding suite of products, and incredibly skilled team have been at the heart of our continued success and our ambition for future growth knows no bounds.

Duetto is building the future of hotel revenue strategy. We’re not just another SaaS company — we’re redefining what’s possible for hotels through our category-creating platform, the Revenue & Profit Operating System.

Introduction:

We are seeking a Lead Data Scientist with deep expertise in forecasting and Bayesian modeling to lead the development of scalable, production-ready machine learning models for demand forecasting and other core challenges in hospitality—such as cancellations, overbooking risk, and pricing response.

In this role, you will design and implement the scientific foundation behind Duetto’s forecasting engine, taking on the unique challenge of building and maintaining thousands of personalized models—one for each hotel partner—tailored to their unique market dynamics and booking behavior.

This is an opportunity for a hands-on, full-stack data scientist who thrives in ambiguity, has strong modeling intuition, and is energized by the challenge of building intelligent systems at scale in a complex, real-world domain.

Key Responsibilities:

• Lead the design, development, and deployment of forecasting and pricing models using a blend of classical time series, deep learning and Bayesian statistical techniques.
• Develop hierarchical forecasting frameworks, including multi-level Bayesian models, that scale across thousands of hotel properties.
• Build uncertainty quantification frameworks to increase trust and robustness in forecasts.
• Guide model architecture choices—balancing complexity, interpretability, and operational feasibility.
• Collaborate closely with engineering to deploy and monitor models in production (e.g., using AWS SageMaker).
• Translate model outputs into actionable insights in partnership with product and business stakeholders.
• Define and execute model performance measurement strategies, including causal inference and uplift modeling.
• Present findings, experimental results, and strategic recommendations to senior leadership.

Qualifications:

• MS or PhD in Statistics, Econometrics, Computer Science, Operations Research, or a related quantitative field.
• 5+ years of experience delivering impactful data science solutions in production environments.
• Expertise in time series forecasting, including classical methods (e.g., ARIMA, Exponential Smoothing, State Space Models) and deep learning (e.g., RNNs, Temporal Fusion Transformers).
• Practical experience with Bayesian modeling, including hierarchical models and probabilistic programming (e.g., PyMC3, Stan).
• Proficiency with ML/DL frameworks (e.g., PyTorch, TensorFlow, scikit-learn, DARTS) and programming languages (Python, R, SQL).
• 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.
• Prior experience in the hospitality, travel, or revenue management domain is highly desirable.
• Experience designing model evaluation and impact measurement frameworks, including causal inference.