(12 / 10 / 2025) 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 :

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