Remote Sr. Data Scientist with Airline Revenue Management
Sr. Data Scientist with Airline Revenue Management
Location - 100% Remote, USA
Mode of Interview - Video
Type Contract
Top Skills:
Airline Revenue Management experience is a must
Google Cloud (Google Cloud Platform) that is the preference but other cloud experience will work.
Programming- Python and C++
Data: PySpark
Experience with Rally and JIRA is good.
Job Description:
Responsibilities
Understanding of airline distribution, pricing, revenue management, NDC and Offer/Order Management concepts.
Work with subject matter experts from airlines to identify opportunities for leveraging data to deliver in-sights 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, or Machine Learning.
Proven ability to apply modeling and analytical skills to real-world problems.
Knowledge of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and statistical concepts (regression, properties of distributions, statistical tests, etc.).
Solid programming skills 2-3 languages out of R, SQL, Python, TensorFlow, PySpark, Java, JavaScript or C++.
Absolutely must have: graduate school level knowledge of Revenue Management models and algorithms.
Experience (minimum 4 out of 7) with deployment of machine learning and statistical models on a cloud:
MLOps within the enterprise CI/CD process for ML models 2 years
Experience deploying ML APIs in production environments in Google Cloud Platform using GKE 2 years
Experience in using Google Cloud Platform Vertex AI for ML and BigQuery 1 year
Knowledge in Terraform and Containers technologies 2 years
Experience writing data processing jobs using Google Cloud Platform Dataflow and Dataproc 2 years
Experience setting up ML model monitoring and autoscaling for ML prediction jobs 1 year
Understanding of machine learning concepts to scale ML across different services by leveraging Feature Store, Artifacts Registry and Analytics Hub 1 year
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