Principal Data Scientist Engineer

Responsibilities

•  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 AI/ML 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 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:

1. MLOps within the enterprise CI/CD process for ML models – 2 years

2. Experience deploying ML APIs in production environments in GCP using GKE – 2 years

3. Experience in using GCP Vertex AI for ML and BigQuery – 1 year

4. Knowledge in Terraform and Containers technologies – 2 years

5. Experience writing data processing jobs using GCP Dataflow and Dataproc – 2 years

6. Experience setting up ML model monitoring and autoscaling for ML prediction jobs – 1 year

7. Understanding of machine learning concepts to scale ML across different services by leveraging Feature Store, Artifacts Registry and Analytics Hub – 1 year

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.