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 GCP using GKE – 2 years
Experience in using GCP Vertex AI for ML and BigQuery – 1 year
Knowledge in Terraform and Containers technologies – 2 years
Experience writing data processing jobs using GCP 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