The company is looking to optimize revenue management processes through data-driven solutions.
Requirements
- Proficiency in Python or R for statistical analysis and machine learning.
- Strong SQL skills and experience with data warehousing.
- Proven experience developing, deploying, and maintaining analytical models in production environments.
- Deep understanding of statistical modeling techniques (e.g., regression, time series, classification, clustering).
- Familiarity with optimization methods (e.g., linear programming, dynamic programming).
- Experience with visualization tools such as Tableau.
- Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP).
Responsibilities
- Develop and implement statistical and machine learning models for revenue forecasting, pricing optimization, and demand prediction.
- Integrate and validate a newly implemented revenue management system, ensuring data accuracy and alignment with business objectives.
- Manage the end-to-end model lifecycle: data collection, cleaning, feature engineering, model training, validation, deployment, and monitoring.
- Build and maintain scalable data pipelines for efficient processing and model training.
- Develop and automate dashboards for tracking KPIs and delivering actionable insights.
- Continuously improve and optimize existing models and analytics processes.
- Perform in-depth data analyses to uncover trends and insights that inform revenue management strategies.
Other
- Collaborate with cross-functional teams to understand business needs and deliver tailored analytical solutions.
- Communicate analytical findings clearly to both technical and non-technical audiences.
- Excellent communication and storytelling skills with the ability to present technical concepts to non-technical stakeholders.
- Self-driven with strong analytical and problem-solving abilities.
- Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Engineering); Master’s or Ph.D. preferred.
- 3–5+ years of experience in data science, ideally in revenue management, operations research, or a related domain.
