Head Analytics & AI

Key responsibilities: Analytics & Data Science Strategy Formulation & Execution : Participate in the conceptualization of AI & Data Science strategy, develop, execute and sustain; strive to build a practice and organization culture around same Manage & Enrich Big Data – Work on Datawarehouse for sales, consumer data, manufacturing data and all the user attributes, think of ways to enrich data (both structured/unstructured) Engage with Data Engineering team on building/maintaining/enhancing Data Lake/Warehouse in MS Azure Databricks (Cloud) Data preparation, new attribute development, preparing Single View of consumers, Single View of Retailers/electricians etc Consumer Insights & Campaign Analysis - Drive adhoc analysis & regular insights from data to generate insights and drive campaign performance Build a Gen AI powered Consumer Insights Factory Support insights from consumer, loyalty, app, sales, transactional data Data mining/AI/ML to support upsell/cross-sell/retention/loyalty/engagement campaigns & target audience identification Purchase behavior/market basket analysis Predictive Analytics & Advanced Data Science - Build & maintain Predictive analytics AI/ML models for use cases in Consumer Domain, Consumer Experience (CX), Service, example: Product recommendations Likely to buy product or services (AMC) Lead scoring conversion Service Risk Scoring or Service Franchise/Technician performance score Likely to be a detractor or Likely to churn Market mix modelling Dashboarding: Build, manage, support various MIS/dashboards via Data Engineering/Visualization team Power BI dashboards & other visualization Adhoc dashboards Analytics & Data Science – Other Domains SCM, Sales Op, Manufacturing, Marketing Support AI/ML models for Sales Transformation or SCM or Marketing Use Cases: Market Mix Modeling Retailer/Electrician loyalty program optimization Retailer/partner risk scoring or churn prediction Product placement & channel partner classification Improve forecast accuracy & Out of Stock prediction Deep data mining to support digital analytics, website behavior, app behavior analytics, call center/CS behavior, NPS, retailer & electrician loyalty etc Gen AI Use Cases : Extensively leverage LLM Models, Agentic AI capabilities to solve business use cases : Chatbot for business users, data mining at fingertips, Chatbot for consumers, Service Voice Agent, Manufacturing use cases etc Data Engineering : Build & support all data engineering work to build /sustain a best-in class efficient, data infrastructure, data lake, datamart on cloud Sustain, optimize current migration of EDW to MS Azure Databricks Build Agentic AI platform on vertex Optimize data architecture for efficient outputs on all analytics areas – data visualization, predictive ML Models, Agentic AI etc Integrate new data sources & data types including unstructured data into Databricks data lake

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