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Tier-1 brand, mid-level experience, and metro locations increase competition, but specialized retail ML focus reduces pool.
Strong retail/CPG domain and econometric modeling requirements limit cross-industry transferability.
Multiple mandatory ML, econometrics, Azure, and retail domain requirements enforce strict shortlisting.
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Define data requirements and develop AI/ML models to drive Data Driven Growth Analytics capability focused on marketing analytics and merchandizing optimizations.
Manage end-to-end data processes including cleaning, aggregation, interpretation, pipeline development, and deployment of scalable machine learning models on cloud platforms (Azure ML tech stack).
Manage client relationships, communicate insights effectively, and act as strategic advisor on data-driven marketing decisions in Retail and CPG sectors.
At least 4+ years work experience in Marketing Analytics with reputed organizations.
Minimum 3+ years experience in Data Driven Merchandizing (Pricing, Promotions, Assortment Optimization) within retail clients.
Bachelor’s or Master’s degree in Statistics, Economics, Mathematics, Computer Science, or related disciplines.
Mandatory technical skills: Statistical timeseries models, store clustering algorithms, state space modeling, mixed effect regression, NLP and large language models, Azure ML, SQL, R, Python, cloud platform experience (Azure/AWS/GCP), data pipelines, non-linear and resource optimization.
Experienced in advanced econometric/statistical modeling and practical applications in Marketing Analytics and Retail/CPG industry contexts.
Proficient in AI/ML model development, deployment, and maintenance on cloud platforms with knowledge of scalable machine learning architecture design patterns.
Skilled in managing client expectations with strong communication skills and ability to translate analytic insights into strategic business recommendations.