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Tier-1 brand and Bengaluru metro raise candidate density despite specialized ML focus.
Strong ML engineering focus is transferable, but airline pricing domain adds moderate specialization.
Explicit 9–12 years plus mandatory ML, big-data, and streaming tech requirements.
Design and develop real-time optimization algorithms and advanced ML models for Dynamic Pricing and Revenue Management to maximize airline revenue.
Build and maintain large-scale data pipelines and ensure data quality, compliance, and reliability.
Support production system implementation, conduct testing, and provide expertise to customers on model results and functionality.
9-12 years of experience in Data Science, Machine Learning, or related roles.
Proficiency in Python, Java, Scala, and PySpark programming languages.
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, Keras, and XGBoost.
Experience with Big Data technologies (Hadoop, Spark, Scala), streaming tools (Kafka), and database technologies including SQL and MongoDB.
Experienced in operating in Agile environments and delivering production-ready ML solutions.
Strong background in mathematics, modeling, and algorithm design focused on optimization problems.
Proficient in integrating data engineering and data science tasks, including building ETL pipelines and working with cloud platforms (Azure) and Databricks (nice-to-have).