Data Scientist Engineer

Skills

Description

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 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 ap-ply 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

• 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 R, SQL, Python, PySpark or other data-extraction and analysis tools and programming languages such as 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.
• Knowledge of airline revenue management algorithms and experience of developing demand forecasting and price optimization models