Job Title

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 recommendations Purchase propensity and AMC/service likelihood Lead scoring and conversion prediction Service risk scoring and technician/franchise performance analytics Churn prediction and detractor likelihood Market mix modeling Conduct 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 modeling Optimization of retailer/electrician loyalty programs Partner risk scoring and churn prediction Product placement and channel partner segmentation Demand forecasting and stockout prediction Digital analytics (web/app behavior), call center/CS performance, NPS, and loyalty analytics GenAI Use Cases Utilize LLMs and agent-based AI to build solutions such as: Internal business chatbots Consumer-facing chatbots Service voice agents Automated data-mining assistants Manufacturing-focused GenAI applications Data 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.