Applied Scientist / ML Engineer

Position:

Location:

Experience required:

About the Role

The individual will play a key role in enhancing and scaling our existing ML systems and developing new capabilities that support our intelligent decision-making platform. The company is 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.

Responsibilities:

● 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.

● 6–10 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.