Senior Data Scientist - Hotel/Hospitality Domain

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 Youll 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 Masters 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 in 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