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Tier-1 brand, generalist data title, metro location, and broad analytics tech stack increase candidate competition.
Core analytics skills transfer across industries, but retail domain expertise and decision-science experience increase sensitivity.
Specific analytics domain plus mandatory tech stack (BigQuery, Spark, Airflow, AI) implies high filtering strictness.
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Lead application of data, analytics, and AI to influence merchandising strategy and business decisions.
Design, develop, and deliver advanced analytical and AI-driven solutions including forecasting, optimization, and automation.
Partner with business stakeholders to understand requirements, present insights, and ensure measurable impact using large-scale data platforms and data pipelines.
Experience with large-scale data platforms such as GCP BigQuery, Spark, and SQL-based warehouses.
Proficiency in building and maintaining data pipelines using orchestration tools like Airflow or similar.
Advanced analytics skills including causal, predictive, and prescriptive methods, plus AI techniques like GenAI and agentic approaches.
Work Experience Required: Not explicitly mentioned in the JD.
Strong proficiency in both technical analytics (AI, machine learning, advanced statistical methods) and business acumen to translate ambiguous problems into structured analytical models.
Experience working in high-impact, cross-domain environments supporting merchandising, supply chain, or retail operations.
Ability to communicate complex analyses clearly to executive and merchant audiences, driving measurable business outcomes and trusted partnerships.