Technical Data Analyst

The Global Supplier Management team at Carlson Wagonlit Travel works on revenue management challenges with various air, hotel, car and rail suppliers. The goal of the team is to optimize and leverage opportunities to grow supplier business (market share). The GSM team's business objects are aligned with those of our suppliers in order to achieve a successful and profitable future for us and our clients. Your role as a Technical Data Analyst will require understanding the underlying data, metrics, and models to allow GSM to be as effective in achieving our objectives as possible.

Responsibilities

• Assist with transformation projects that aid the development of a best in class revenue management capability.
• Interpreting data, analyzing results in complex data sets using statistical techniques
• Developing and implementing data analyses, data collection systems and other strategies that optimize statistical efficiency and quality
• Supporting the development of predictive market share models and future data science projects
• Understanding, demonstrating, and communicating the impact of data analysis and results on business impacts and business drivers.

Required Qualifications:

• Bachelor's or higher in data science, mathematics, statistics, computer science, or analytics related degree.
• Passionate about problem solving and solving data puzzles
• Capable of working both independently and collaboratively
• Able to investigate research and diagnose problems and potential solutions

Preferred Qualifications:

• Experience with SQL based technologies and working with databases
• Ability to interpret, assess, and analyze data for trends, anomalies, and oddities
• Ability to communicate effectively with non-technical individuals
• Familiarity with Descriptive Statistics

Could Haves

• Understanding of Airline Industry
• Understanding of data science and data analytics concepts
• Good grasp of how to use data and visualizations to tell stories
• Familiarity with good programming practices
• Demonstrated ability to execute
• In depth understanding of machine learning algorithms and underlying statistical methods that govern them

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