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
