TakeUp is looking to solve the problem of revenue optimization for hospitality using AI and machine learning, specifically by developing a rate optimization engine that can unlock maximum revenue potential through pricing courageously
Requirements
- 2+ years of experience building and deploying ML models to production
- Proficiency in Python (scikit-learn, XGBoost/LightGBM/CatBoost)
- Strong SQL skills and comfort with large, imperfect, real-world datasets
- Experience across the model lifecycle: data preparation, feature engineering, training, evaluation, deployment, and monitoring
- Familiarity with experimentation design and metrics; able to reason about trade-offs and safeguards
- Experience with causal inference (uplift modeling, causal forests, DML) or time-series modeling
- Exposure to reinforcement learning concepts (multi-armed bandits, dynamic programming, temporal-difference learning)
Responsibilities
- Train and deploy production ML models, with emphasis on tree-based methods (XGBoost, LightGBM, CatBoost)
- Contribute to feature engineering and model evaluation approaches that balance accuracy, interpretability, and speed
- Translate customer needs and product requirements into practical modeling tasks (classification, regression, ranking, uplift)
- Help develop performance metrics to evaluate models’ ability to generate reasonable counterfactual demand estimates while accounting for varying customer product expectations
- Build and maintain training and evaluation pipelines, ensuring offline metrics align with online business outcomes
- Partner with engineering to integrate models into production services and batch jobs with defined SLAs
- Write production-ready Python and SQL code with testing and review practices
Other
- 2+ years of experience
- Clear communicator, comfortable working in a collaborative, fast-paced environment
- Travel may be needed for customer discovery, team building, and/or networking
- Prior start-up experience
- Domain knowledge in pricing, demand modeling, or hospitality
