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Global brand, generic Data Science title, metro location, and broad skillset drive high applicant density.
Core ML and big-data skills are transferable, but airline revenue optimization domain adds specificity.
Explicit 9–12 years plus specific ML, Big Data, and tech-stack requirements make shortlisting highly strict.
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Design and develop advanced 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, governance, and security compliance.
Train, calibrate, and industrialize ML models; conduct performance measurement and provide expertise supporting customers on model results.
9–12 years experience in Data Science, Machine Learning, or related roles.
Strong programming skills in Python, Java, Scala, and PySpark.
Experience with ML frameworks such as PyTorch, TensorFlow, Keras, and XGBoost.
Proficiency with Big Data technologies (Hadoop, Spark, Scala) and streaming tools like Kafka.
Experienced in building and managing ETL/data pipelines with solid SQL and NoSQL (MongoDB) knowledge.
Familiar with cloud platforms (Azure), Databricks, and data visualization tools (Tableau, Power BI).
Comfortable working in Agile environments and collaborating across technical teams in a data science and engineering context.