Principal Data Scientist

Title: Revenue Management Data Scientist, Revenue Optimization Data Scientist, ? Pricing and Revenue Optimization Analyst, Revenue Forecasting Data Scientist, Operations Research Scientist (Revenue Management), Data Scientist - Revenue Management. Hit Apply below to send your application for consideration Ensure that your CV is up to date, and that you have read the job specs first. Job Summary: Alaska Airlines is seeking a highly skilled Data Scientist with a focus on revenue prediction and dynamic pricing. The ideal candidate will have a strong foundation in predictive modeling, data analysis, and optimization, specifically tailored to the airline industry. You will work closely with the operations and revenue management teams to develop models and strategies that enhance pricing, revenue forecasting, and operational efficiencies. Key Responsibilities: Dynamic Pricing Optimization: Develop and implement dynamic pricing models that adapt to market conditions and customer demand to maximize revenue. Revenue Prediction Modeling: Build robust revenue forecasting models that account for various factors such as seasonality, customer behavior, and operational constraints. Airline Itinerary Scheduling: Collaborate with operations teams to integrate predictive models into itinerary and scheduling systems, optimizing for revenue and operational efficiency. Forecasting & Operations Research: Use advanced forecasting techniques and operations research methods to inform strategic decision-making. Data Intake & Management: Collect, process, and analyze large datasets from multiple sources, ensuring accuracy and completeness of data used for modeling. Databricks & PySpark Expertise: Utilize PySpark and Databricks for large-scale data processing, model development, and deployment. Tuning Spark Services: Optimize Spark services for efficient data processing and model execution. Programming in Groovy or Ruby: Leverage Groovy or Ruby scripting languages for pipeline automation and data integration tasks. Reference Curve Optimization: Apply reference curve optimization techniques to fine-tune revenue models based on historical data and market conditions. Mixed Integer Programming: Use advanced optimization techniques such as mixed integer programming to solve complex operational and scheduling problems. Qualifications: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Operations Research, or a related field. Proven experience in revenue prediction, dynamic pricing, or operations research, ideally within the airline or travel industry. Strong hands-on experience with PySpark and Databricks for data engineering and model development. Proficiency in programming languages such as Python, Groovy, or Ruby. Deep understanding of forecasting methods, statistical modeling, and machine learning algorithms. Experience with optimization techniques, including mixed integer programming and reference curve optimization. Ability to work with large datasets and streamline data pipelines for real-time model implementation. Strong analytical skills and the ability to translate data insights into business recommendations. Excellent communication skills and ability to collaborate with cross-functional teams, including operations, revenue management, and IT. Keywords: Dynamic Pricing Revenue Management Revenue Forecasting Predictive Modeling Time Series Analysis Airline Itinerary Scheduling Operations Research Optimization Algorithms Mixed Integer Programming (MIP) Reference Curve Optimization Databricks PySpark Apache Spark Data Engineering Big Data Processing Machine Learning (ML) Statistical Modeling Data Intake & Management Data Cleaning Python (Pandas, NumPy, Scikit-learn) Groovy Ruby SQL Cloud Computing (AWS, GCP, or Azure) Airline Revenue Optimization Pricing Strategy Large-scale Data Analysis Spark Tuning Experience and Background: Experience working in airline revenue management, travel industry, or e-commerce pricing. Background in forecasting models for dynamic pricing or demand prediction. Experience in large-scale data environments (e.g., using Databricks, Spark). Past roles involving operations research and optimization techniques in a complex, data-driven environment. Hands-on work with machine learning models, particularly in pricing and revenue forecasting. Demonstrated experience in statistical modeling and building robust predictive analytics systems. Experience working with large datasets for pricing strategies and revenue forecasting. Familiarity with cloud platforms (especially Databricks or Spark on AWS/Azure/GCP). Knowledge of airline-specific or travel industry terms like yield management, load factor optimization, or inventory control. Industries and Relevant Employers: Airlines or Travel Companies (e.g., Alaska Airlines, Delta, Southwest Airlines) E-commerce or online marketplaces (with a focus on pricing and revenue) Data Science or Analytics roles within the transportation, logistics, or hospitality sectors Experience at companies that specialize in dynamic pricing or revenue management systems