Principal Data Scientist

General Business environment
In the airline organization, Revenue Management and Dynamic Pricing are very strategic areas.
Revenue Management has greatly evolved during the past 20 years, from the early days of leg-based optimization to today's complex network Revenue Management and bid-price control. It will remain a strategic area of investment for the years to come. In a very competitive market, state of the art revenue management capabilities is a key differentiator and significantly contribute to airline profitability. In addition, the shift of the industry towards NDC and the offer/order distribution models, is putting ahead yet another Offer optimization challenge and tremendous amount of new scientific complexity.

Dynamic Pricing is a young and promising product to optimize in real time the price returned during a shopping session, depending on the customer segmentation and the competition environment, with airlines eager to pilot and benefit from those extra revenues.
Combined with our latest algorithms proven through advanced modelling on up-to-date airline business scenarios, and the unbeatable advantages of tight integration with Inventory and the entire Altéa Suite, Amadeus proposes one of the airline industry’s best revenue management and dynamic pricing solutions.

This science is powered by a big amount of data and needs to be controlled by the end users with efficient interfaces and workflows.

Offer Optimization context

This position consists specifically in developing the models of the Dynamic Pricing & Revenue Management applications executed in real time to perform price optimization.

Accountabilities

• Analyze specifications, help to define user requirements for the development of software solutions.

• Design and development.

  • Design technical solutions and perform feasibility studies.

  • Propose viable technical solutions.

  • Develop software according to Amadeus standards.

• Data Engineering Activities

  • Acquire datasets that align with business needs.

  • Develop algorithms to transform data into useful, actionable information.

  • Design, develop, construct, test, and maintain optimal data pipeline architectures.

  • Create new data validation methods and data analysis tools.

  • Ensure compliance with data governance and security policies.

  • Identify ways to improve data reliability, efficiency, and quality.

• Data Science activities

  • ML Model training.

  • ML Model calibration for different customers

  • KPI definition and measurements.

  • Data mining, data analysis

• Research

  • Design new algorithms / models. Enhance the existing ones.

  • Industrialize and implement new prototypes. 

• Test and support

  • Conduct unit, package and performance tests and ensure a level of quality in line with the Amadeus guidelines.

  • Participate in the validation phase of the product cycle, fine-tuning when necessary to finalize the product.

  • Support the customer, perform investigation about the model result and behavior.

  • Documentation

Skills

- Python/Java/Scala/PySpark.

- Applied ML experience (PyTorch, Tensorflow, Kera, XGBoost…)

- Kafka

- SQL , MongoDB

- Tableau / PowerBI

- Interest in Mathematics and Modelling.

- Big Data: Hadooop-Spark-Scala knowledge. Strong Data Engineering Skills

- Knowledge on Cloud/Azure/Databricks

Diversity & Inclusion

Amadeus aspires to be a leader in Diversity and Inclusion in the tech industry, enabling every employee to reach their full potential by fostering a culture of belonging and fair treatment, attracting the best talent from all backgrounds, and as a role model for an inclusive employee experience.  

Amadeus is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to gender, race, ethnicity, sexual orientation, age, beliefs, disability or any other characteristics protected by law.