Data Scientist

Applied Scientist / Machine Learning Engineer

We are looking for team members who:

• Are deeply curious and passionate about applying machine learning to real-world problems.
• Demonstrate strong ownership and the ability to work independently.
• Excel in both technical execution and collaborative teamwork.
• Have a track record of shipping products in complex environments.

What You’ll Do

• Build, train, and deploy machine learning models for forecasting, pricing, and optimization.
• Apply advanced techniques like causal inference, counterfactual analysis, and reinforcement learning to improve decision-making under uncertainty.
• Work with large-scale, noisy, and temporally complex datasets.
• Collaborate cross-functionally with engineering and product teams to move models from research to production.
• Design offline evaluation frameworks and simulations to validate new algorithms before live rollout.
• Generate interpretable and trusted outputs to support adoption of AI-driven rate recommendations.
• Contribute to the development of an AI-first platform that redefines hospitality revenue management.

Required Qualifications

• Bachelor's or Master’s degree in Computer Science or related field.
• 5–10 years of hands-on experience in a product-centric company, ideally with full model lifecycle exposure.
• Demonstrated ability to apply machine learning to solve real-world business problems.
• Proficient in Python and machine learning libraries such as scikit-learn, PyTorch, and XGBoost.
• Strong knowledge of forecasting models (time-series and ML-based).
• Deep understanding of machine learning and deep learning foundations.
• Comfort with optimization under uncertainty and experience evaluating ML model performance rigorously
• Ability to work independently and manage projects end-to-end.

Preferred Experience

• Experience in revenue management, pricing systems, or demand forecasting, particularly within the hotel and hospitality domain.
• Applied knowledge of reinforcement learning techniques (e.g., bandits, Q-learning, model-based control).
• Familiarity with causal inference methods (e.g., DAGs, treatment effect estimation).
• Strong written and verbal communication skills to explain complex technical concepts clearly to cross-functional teams.
• Proven experience in collaborative product development environments, working closely with engineering and product teams.