DATA SCIENCE ENGINEER

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 statistical and machine learning techniques leveraging huge volumes and variety of consumer and competitive data available in airline industry.

The Data Science Engineer 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 multiple different data sources, write high-quality data manipulation scripts in R, Python, Perl, bash, etc, develop and apply data mining and machine learning algorithms for advanced analysis and prediction. 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

Minimum 5+ years in Data Science Engineer and able to Work with subject matter experts from airlines 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.
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.
Collaborate with software engineers to implement and test production quality code for forecasting and data analytics models.
Develop processes and tools to monitor and analyze data accuracy and models performance.
Demonstrate 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 improvements and enhancements.

Required Qualifications

Advanced Degree in Statistics, Operations Research, Computer Science, Mathematics, Machine Learning or related Quantitative disciplines.
Proven ability to apply modeling and analytical skills to real-world problems.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Solid programming skills with knowledge of SQL, Python, PySpark or other data-extraction and analysis tools and programming languages golang (preferred), Java, JavaScript or C++.
Experience with deployment of machine learning and statistical models on a cloud and leveraging services like Amazon SageMaker and Amazon Forecast.

Desirable Qualifications

Familiarity with airline, hospitality or retailing industries and decision support systems employed there.
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.

Carrier Ladder

Level

Education & Experience

Scope & Complexity

Masters + 2 years

Diverse/moderately complex work is completed under minimal supervision, internal and external interaction, may lead projects. Employee can become proficient via a learned upper education curriculum such as a 4-year undergraduate degree as well as prior work experience in this or similar discipline. Manager makes assignments by defining objectives, priorities and deadlines, and assists the employee with unusual situations which do not have clear objectives. Employee plans and carries out successive steps and resolves problems and deviations in accordance with instructions, policies and accepted practices. Supervisor reviews the work for technical adequacy and conformance with practice and policy.

Sr Data Science Engineer