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