OPERATIONS RESEARCH SCIENTIST – Revenue Management

Sabre is a technology company that powers the global travel industry. By leveraging next-generation technology, we create global technology solutions that take on the biggest opportunities and solve the most complex challenges in travel. 

Positioned at the center of the travel, we shape the future by offering innovative advancements that pave the way for a more connected and seamless ecosystem as we power mobile apps, online travel sites, airline and hotel reservation networks, travel agent terminals, and scores of other solutions.

Simply put, we connect people with moments that matter.

OPERATIONS RESEARCH SCIENTIST – Revenue Management 

Airline industry is going through a drastic transformation in the area of retailing and distribution that requires very advance data analytics support to optimize revenue performance and customer experience. Recently introduced concepts of Offer/Order Management and Continuous Dynamic Pricing significantly expand opportunities for engaging with travelers through multiple touch points and creating personalized offers accounting for individual preferences and market context. These practices can substantially benefit from a combination of mathematical and statistical modeling and machine learning techniques leveraging huge volumes and variety of consumer and competitive data available in airline industry. 

The Operations Research Scientist applies expert level statistical analysis, data modeling, and predictive analysis on strategic and operational problems in airline industry. As a key member of the Sabre Operations Research team, you will leverage your statistical and business expertise to translate business questions into data analysis and models, define suitable KPIs, and graphically present results to a wide range of audiences including internal and external clients, sales, and development team. In addition, you will source data from a variety of data sources, write high-quality data manipulation scripts in primarily Python, Pyspark, C++, SQL, and/or R, develop and apply data mining and machine learning algorithms for advanced analysis, prediction and prescriptive models. You will also utilize your strong communication skills to work with developers to support product development cycles and decision makers who need empirical data to promote sales and growth. 

Responsibilities 

• 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:

- MLOps within the enterprise CI/CD process for ML models 

- Experience deploying ML APIs in production environments in GCP using GKE

- Experience deploying ML APIs in production environments in GCP using GKE

- Experience in using GCP Vertex AI for ML and BigQuery 

- Knowledge in Terraform and Containers technologies

- Experience writing data processing jobs using GCP Dataflow and Dataproc

- Experience setting up ML model monitoring and autoscaling for ML prediction jobs

- 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. 

The level of the position will be determined based on the candidate's qualifications and experience.

Benefits:

• Attractive remuneration model

• 30 days of vacation + additional days off between Christmas and New Year

• Monthly meal allowance

• Flexible working arrangements

• 4 volunteer days per year that you can use for a charity of your choice

• Numerous employee development events, including learning platforms/tools

We will give careful consideration to your application and review your details against the position criteria. You will receive separate notification as your application progresses. Please note that only candidates who meet the minimum criteria for the role will proceed in the selection process.

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