Data Scientist

Job Title

Data Scientist

Summary of the role

This position will be under the Airfare & Ancillary Pricing Optimization (APO) product team at Navitaire. Airlines around the globe use APO to run and analyze randomized pricing experiments, enable rule-based and dynamic pricing, as well as to leverage machine learning to implement the most innovative pricing strategies. As a data scientist on the APO team, you will work closely with customers, product management and development teams, and you will drive innovation by identifying cutting edge revenue optimization techniques and implement them for Navitaire's customers worldwide. You will see your model builds through from the raw data all the way to production and monitoring.

This is an exciting time to join the APO team, as Navitaire is currently undertaking a full cloud transformation of its entire application suite. You will be given the unique opportunity to leverage the cloud to build and deploy machine learning models at scale, create data visualizations, improve automation, grow the use of analytics and machine learning at Navitaire, and explore new solutions for Navitaire's dozens of customers.

Responsibilities

• Build and deploy machine learning models to achieve objectives such as increasing revenue and conversion rates.
• Use a combination of data aggregation and visualization techniques to help customers understand and leverage their data.
• Guide Navitaire's customers to effectively run and analyze randomized pricing experiments.
• Provide training to customers on how to get the most out of APO with machine learning and statistical modeling.
• Assist and mentor other data analysts and data scientists at Navitaire.
• Become a domain expert on the analytical opportunities to be explored within Navitaire and customer data.

Required qualifications

• A BSc or equivalent in Statistics, Mathematics, Economics, Physics, or other quantitative discipline.
• 2+ years of experience in data science, including building machine learning models.
• Expertise in exploratory data analysis as well as machine learning algorithms such as linear/logistic regression, decision trees, random forest or neural networks.
• Well-rounded Python experience is a must, particularly using packages such as NumPy, scikit-learn, pandas, PyTorch, TensorFlow or XGBoost.
• Knowledge of SQL and relational database concepts.
• Strong communication skills.

Preferred qualifications

• A MSc, PhD or equivalent in Statistics, Mathematics, Economics, Physics, or other quantitative discipline.
• Industry experience in the airline, transportation, travel or hospitality sectors.
• Understanding of revenue and/or yield management.
• Experience designing A/B tests or randomized experiments and analyzing the results.
• Experience in causal inference or treatment effect modeling.
• Prior exposure to distributed and/or cloud computing, such as Microsoft Azure, AWS or Databricks.
• Knowledge of R and/or C#.
• Experience in BI and visualization tools, such as Power BI or Tableau.

What we can offer you

• The opportunity to work for one of the world's top leading travel tech companies; a company that originated in technology innovation and sees the world with a technology-first perspective

• Skills development and opportunities to try new ideas
• A global diverse work environment

Application process:

The application process takes no longer than 10 minutes! Create your candidate profile, upload your CV and apply.

Are you the one we are looking for? Apply now!

Diversity & Inclusion

Amadeus is an Equal Employment Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or a related medical condition), ancestry, national origin, age, genetic information, military or veterans status, sexual orientation, gender expression, perception, or identity, marital status, mental or physical disability status, or any other protected federal, state, or local status unrelated to performance of work involved.