Data Scientist - Revenue Management

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.

Required Skills & Experience:

• 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.

Preferred Qualifications:

• Experience with visualization tools such as Tableau.

• Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP).

• Solid understanding of revenue management principles and practices.