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Specialized ML role with moderate brand and mid-level seniority leads to medium applicant density.
Technical ML and Databricks skills transfer across industries though Retail/CPG preference raises domain sensitivity.
Explicit 6+ years requirement and mandatory PySpark/Databricks/MLFlow/cloud skills enforce high shortlisting strictness.
Design, optimize, and maintain scalable ETL pipelines using PySpark and Databricks on Azure/GCP cloud platforms.
Build, tune, and operationalize ML/AI and optimization models for Retail/CPG business applications like demand forecasting, price elasticity, and inventory allocation.
Communicate technical model insights and root cause analyses to business stakeholders; manage cloud resource consumption costs effectively.
Master’s degree in engineering, computer science, data science, operations research, statistics, mathematics, quantitative sciences or equivalent experience.
6+ years of professional experience in Data Science/Data Engineering with full lifecycle experience in Data Science-ML/AI projects.
Proficiency in Python/PySpark, SQL, cloud platforms (AWS, Azure, or GCP), Databricks, and production-grade data and ML/AI pipeline development.
Experience with Git for code management, orchestration tools like Databricks or Airflow, plus knowledge of Retail/CPG domain challenges is highly desirable.
Experienced in deploying and optimizing large-scale data engineering and ML pipelines in cloud environments with cost-efficient resource management.
Strong domain expertise in Retail/CPG Supply Chain, Pricing, Promotions, Inventory, and workforce optimization use cases.
Able to translate complex technical findings into clear business insights for stakeholders and work independently in fast-paced, innovation-driven settings.