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Popular mid-level ML/data role with moderate brand, though niche retail forecasting reduces applicant density.
Core ML and engineering skills transferable, but retail/CPG forecasting domain knowledge increases sensitivity.
Explicit 3+ years plus production forecasting, PySpark/Databricks, and cloud requirements enforce moderate filtering.
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Design, optimize, and maintain scalable ETL and production ML/AI pipelines for retail/CPG demand forecasting using PySpark, Databricks, and cloud platforms (Azure/GCP).
Develop and implement automated data validation and quality checks; optimize cloud resource allocation to control costs.
Collaborate frequently with business stakeholders and cross-functional teams to align forecasting models and solutions with business needs and explain model performance and decisions.
Master’s degree in engineering, computer science, data science, operations research, statistics, mathematics, quantitative sciences, or relevant experience.
Minimum 3+ years in Data Science or Data Engineering focusing on demand forecasting or time-series modeling projects including production deployment and monitoring.
Strong expertise in Python/PySpark, SQL, relational or NoSQL databases, and cloud resource management on AWS, Azure, or GCP.
Experience with production-grade ML pipelines, Databricks or similar orchestration tools, and Git for code management.
Proven background in designing and operating production demand forecasting systems using statistical and regression-based models at retail/CPG scale.
Experience working directly with retail/CPG stakeholders for demand forecasting, promotional planning, or supply chain analytics.
Strong communicator capable of explaining technical details and model behavior to non-technical business stakeholders and customer planners.