Data Science Analyst (Senior)

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 year
2.Experience deploying ML APIs in production environments in GCP sing GKE – 2 years
3.Experience in using GCP Vertex AI for ML and BigQuery – 1 yea
4.Knowledge in Terraform and Containers technologies – 2 years
5.Experience writing data processing jobs using GCP Dataflow nd 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