OPERATIONS RESEARCH SCIENTIST – Revenue Management

• Work with subject matter experts with airline industry experience to identify opportunities for leveraging data to deliver insights and actionable prediction of customer behavior and operations performance. 
• Assess the effectiveness and accuracy of new data sources, data gathering and forecasting techniques. 
• Evaluate merits of different optimization algorithms or heuristic solutions against each other 
• Develop custom data models and algorithms to apply to data sets and run proof of concept studies. 
• Leverage existing Statistical and Machine Learning tools to enhance in-house algorithms. 
• Develop own production quality code and/or collaborate with software engineers to implement and test production quality code for traditional OR or AI/ML models. 
• Develop processes and tools to monitor and analyze data accuracy and models’ performance. 
• Demonstrate models/algorithms/software to customers and perform value proving benchmarks. Calibrate software for customer needs and train customer for using and maintaining software. 
• Resolve customer complaints with software and respond to suggestions for enhancements. 

Required Qualifications 

• Advanced degree in Statistics, Operations Research, Mathematics, Applied Math, or Data Science or in a field such as Engineering, Computer Science, Economics or Business that leverages such scientific methods. 
• Proven ability to apply modeling and analytical skills to real-world problems. 
• Knowledge of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and statistical concepts (regression, properties of distributions, statistical tests, etc.). 
• Solid programming skills 2-3 languages out of Python, PySpark, SQL, TensorFlow, C++, R, or Java. 
• Absolutely must have: Graduate school degree in one of the above programs and knowledge of Revenue Management models and algorithms. 

Desirable Qualifications 

• Experience with deployment of machine learning and statistical models on a cloud is preferred:

1.MLOps within the enterprise CI/CD process for ML models 

2.Experience deploying ML APIs in production environments in GCP using GKE 

3.Experience in using GCP Vertex AI for ML and BigQuery 

4.Knowledge in Terraform and Containers technologies 

5.Experience writing data processing jobs using GCP Dataflow and Dataproc 

6.Experience setting up ML model monitoring and autoscaling for ML prediction jobs 

7.Understanding of machine learning concepts to scale ML across different services by leveraging Feature Store, Artifacts Registry and Analytics Hub 

• Familiarity with airline, hospitality, retailing, more specifically perishable product retailing and decision support systems. 
• Experience developing customer choice models, price elasticity estimation and market potential estimation. 
• Understanding of airline distribution, pricing, revenue management, NDC and Offer/Order Management concepts.