Data Scientist - Revenue Management

Job Title: Data Scientist – Revenue Management

Location: Fort Lauderdale

• Develop and implement statistical and machine learning models for revenue forecasting, pricing optimization, and demand prediction.
  


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  Integrate and validate a newly implemented revenue management system, ensuring data accuracy and alignment with business objectives.



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  Manage the end-to-end model lifecycle: data collection, cleaning, feature engineering, model training, validation, deployment, and monitoring.



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  Collaborate with cross-functional teams to understand business needs and deliver tailored analytical solutions.



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  Perform in-depth data analyses to uncover trends and insights that inform revenue management strategies.



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  Build and maintain scalable data pipelines for efficient processing and model training.



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  Communicate analytical findings clearly to both technical and non-technical audiences.



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  Develop and automate dashboards for tracking KPIs and delivering actionable insights.



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  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:

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  Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Engineering); Master’s or Ph.D. preferred.



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  3–5+ years of experience in data science, ideally in revenue management, operations research, or a related domain.



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  Proficiency in Python or R for statistical analysis and machine learning.



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  Strong SQL skills and experience with data warehousing.



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  Proven experience developing, deploying, and maintaining analytical models in production environments.



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  Deep understanding of statistical modeling techniques (e.g., regression, time series, classification, clustering).



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  Familiarity with optimization methods (e.g., linear programming, dynamic programming).



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  Excellent communication and storytelling skills with the ability to present technical concepts to non-technical stakeholders.



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  Self-driven with strong analytical and problem-solving abilities.

Preferred Qualifications:

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  Experience with visualization tools such as Tableau.



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



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  Solid understanding of revenue management principles and practices.