Applied Scientist - Machine Learning

Description :

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.

Responsibilities :

- Build, train, and deploy machine learning and operations research models for forecasting,

pricing, and inventory optimization.

- Work with large-scale, noisy, and temporally complex datasets.

- Collaborate cross-functionally with engineering and product teams to move models from

research to production.

- Generate interpretable and trusted outputs to support the adoption of AI-driven rate

recommendations.

- Contribute to the development of an AI-first platform that redefines hospitality revenue

management.

Requirements :

- Bachelor's or Master's degree or PhD in Computer Science or related field.

- 3-5 years of hands-on experience in a product-centric company, ideally with a full model

lifecycle exposure.

- Acceptable Degree types - Master's or PhD, Fields, Operations Research, Industrial/Systems

Engineering, Computer Science, Applied Mathematics.

- Demonstrate ability to apply machine learning and optimization techniques to solve real-

world business problems.

- Proficient in Python and machine learning libraries such as PyTorch, statsmodels, LightGBM,

scikit-learn, and XGBoost.

- Strong knowledge of Operations Research models (Stochastic optimization, dynamic

programming) and forecasting models (time-series and ML-based).

- Understanding of machine learning and deep learning foundations.

- Translate research into commercial solutions.

- Strong written and verbal communication skills to explain complex technical concepts clearly

to cross-functional teams.

- 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).

- Proven experience in collaborative product development environments, working closely with

engineering and product teams.