AVP/ JVP Analytics

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.