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Well-known global brand and metro location increase applicant density, but senior niche specialization limits competition.
Strong ML engineering skills are transferable, but airline revenue management domain expertise increases fit sensitivity.
Explicit 9–12 years and many mandatory ML, big-data, and tooling requirements make shortlisting highly strict.
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Design and develop advanced models and real-time optimization algorithms for Dynamic Pricing and Revenue Management to maximize airline revenue.
Build and maintain large-scale data pipelines and ensure data quality and security compliance.
Train, evaluate, and industrialize machine learning models, while supporting customers with model insights and expertise.
9–12 years of experience in Data Science, Machine Learning, or related roles.
Strong programming skills in Python, Java, Scala, and PySpark.
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, Keras, and XGBoost.
Proficiency with Big Data technologies including Hadoop, Spark, Scala, Kafka; SQL and MongoDB; experience with ETL pipelines.
Experienced in combining data science, engineering, and research to solve complex optimization problems in competitive domains like airline revenue management.
Comfortable working with Agile methodologies and collaborating across teams to deliver production-ready machine learning solutions.
Strong mathematical and algorithmic modeling skills, with a focus on operationalizing ML models and data pipelines at scale.