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Tier-1 brand, mid-level generalist ML title, metro location, and broad skill requirements increase competition.
Core ML skills transfer, but retail merchandising, elasticity, and planogram domain expertise increase fit sensitivity.
Explicit 4+ years plus mandatory forecasting, optimization, and tech stack (Python, SQL, Spark) make filters strict.
Develop and improve sales forecasting, elasticity, and optimization models to recommend Planogram item placements that balance sales, margin, guest value, and business constraints.
Deploy scalable predictive and optimization solutions to production, monitor model performance, and iterate to maintain quality and impact.
Collaborate with cross-functional teams including Data Scientists, Product Managers, Engineers, and Merchandising partners to translate business needs into data-driven modeling solutions.
Bachelor’s, Master’s, or PhD in Data Science, Statistics, Economics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative field.
4+ years of experience in data science or applied machine learning including forecasting, elasticity modeling, optimization, and retail domain knowledge.
Strong programming skills in Python and SQL with hands-on experience in large-scale data platforms such as Spark, PySpark, Hive, or Hadoop.
Experience building and validating machine learning/statistical models, forecasting, elasticity modeling, and optimization; experience in production deployment and experimentation frameworks.
Experienced in retail merchandising analytics focusing on demand forecasting, elasticity, and constrained optimization.
Skilled at working with large-scale structured datasets and translating ambiguous business problems into analytical solutions.
Familiar with advanced retail measurement approaches such as A/B testing, incrementality, plus emerging AI capabilities like Generative AI, LLMs, or AI workflow automation for decision support.