Job Title: Data Scientist – Revenue Management

Location: Fort Lauderdale
Job Type: Contract to Hire (3 Months)

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.
Collaborate with cross-functional teams to understand business needs and deliver tailored analytical solutions.
Perform in-depth data analyses to uncover trends and insights that inform revenue management strategies.
Build and maintain scalable data pipelines for efficient processing and model training.
Communicate analytical findings clearly to both technical and non-technical audiences.
Develop and automate dashboards for tracking KPIs and delivering actionable insights.
Continuously improve and optimize existing models and analytics processes.
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.

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.
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).
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.

Data Scientist - Revenue Management