AVP / JVP - Analytics & Data Science
Strategy & Practice Development
- Contribute to the design, execution, and continuous improvement of the AI and Data Science strategy.
- Help build and nurture a data-driven culture and a strong analytics practice within the organization.
Big Data Management & Enrichment
Work with large datasets across sales, consumer, manufacturing, and user attributes.Identify opportunities to enrich both structured and unstructured data to improve analytical outcomes.Collaborate with Data Engineering teams to build, enhance, and maintain cloud-based Data Lakes / Warehouses (MS Azure Databricks).Data Preparation & Single View Creation
Prepare datasets, develop new data attributes, and create unified consumer, retailer, and electrician profiles.Consumer Insights & Campaign Analytics
Lead ad-hoc and ongoing analyses to uncover insights and improve campaign performance.Develop a GenAI-powered Consumer Insights Factory to automate and scale insights generation.Support insight generation across consumer, loyalty, app, sales, and transactional data.AI / ML Applications & Predictive Analytics
Build and maintain predictive AI / ML models across key consumer, CX, and service-related use cases, such as :Product recommendationsPurchase propensity and AMC / service likelihoodLead scoring and conversion predictionService risk scoring and technician / franchise performance analyticsChurn prediction and detractor likelihoodMarket mix modelingConduct deep data mining to support upsell, cross-sell, retention, loyalty, and audience segmentation initiatives.Dashboarding & Reporting
Work with Data Engineering and Visualization teams to deliver MIS reports and dashboards.Develop and support Power BI dashboards and other ad-hoc visualizations.Analytics & Data Science – Cross-Functional Domains (SCM, Sales Operations, Manufacturing, Marketing)
Support AI / ML solutions across multiple business functions, including :
Market mix modelingOptimization of retailer / electrician loyalty programsPartner risk scoring and churn predictionProduct placement and channel partner segmentationDemand forecasting and stockout predictionDigital analytics (web / app behavior), call center / CS performance, NPS, and loyalty analyticsGenAI Use Cases
Utilize LLMs and agent-based AI to build solutions such as :Internal business chatbotsConsumer-facing chatbotsService voice agentsAutomated data-mining assistantsManufacturing-focused GenAI applicationsData Engineering Responsibilities
Support and improve all data engineering activities to maintain a robust cloud-based data infrastructure encompassing data lakes and data marts.Sustain and optimize the migration of the enterprise data warehouse to MS Azure Databricks.Build an Agentic AI platform on Vertex.Enhance data architecture for efficient visualization, ML modeling, and GenAI use cases.Integrate new structured and unstructured data sources into the Databricks ecosystem.