I work for Walmart international division (Other Two divisions are Sam’s Club and Walmart US) on data projects for international markets such as Canada, Chile, Central America, India, U.K, China and South Africa 
 
*The data projects I work on cover multiple areas: Customer data, Supply chain/Inventory and Fulfillment data, Store operational data and Ecommerce Total loss prevention(eTLP) 
 
*Customer data: For multiple International markets: Canada, Chile, South Africa, U,K, India, Central America and China. Ingestion of Customer ecommerce data, Customer store transactional data, Customer contact center data, Customer Survey data, Customer feedback data, associate(employees of Walmart) discount data, Loyalty data of customers, Sam’s membership data(For China and Mexico markets) in Walmart enterprise data lake. This data includes hundreds of millions of records. Combining all international markets there is around 400 million records of customer data(400 million customers). Once this data is ingested: Implement matching algorithm on the ingested sources by customer matching of profile elements :Name, address, Phone number, physical address and email address of customer and implement confidence score. For example: We are confident that the customer is matching up to 80% and 90% etc.., based on their profile elements such as name, address, phone number, email using Machine learning model. Once matching is complete: Perform data science activities by segmenting customer data, data analytics activities to perform targeted marketing of customers via SMS, Email and App notifications 
 
Use cases for customer data for Walmart: 
 
–Personalization: Analyzing customer data creating more personalized marketing campaigns, product recommendations and customer service interactions. 
–Segmentation: Segmenting customers into different groups based on demographics, purchase history, and other factors. This will help in targeting marketing efforts more effectively. 
–Predictive modeling: Predictions about future behavior: Which customers are most likely to make a purchase or which products are likely to be popular. 
–Churn analysis: Identify patterns in customer behavior that indicate the risk of leaving the business and hence taking proactive steps to retain those customers. 
–Identifying cross-selling opportunities: Analyzing customer data and identify patterns that indicate when a customer is likely to be interested in purchasing related products or services. 
–Improving customer service: Identify common customer service issues and improve their processes to better meet customer needs. 
–In-store or online targeting: Target omnichannel experience of in-store or online promotions and offers to the specific customers who are most likely to be interested in them. 
 
Overall, using customer data points can helps Walmart to make more informed decisions, improve operations and better serve the customers. 
 
*Supply Chain Data: For multiple International markets: Canada, Chile, South Africa, U,K, India, Central America and China. Ingestion of Supply chain Distribution Center(DC) data, Walmart 3rd party seller data for Walmart Fulfillment services(Similar to Fulfilled by Amazon), Supply chain metrics – Dock to Stock, Number of shipments per Distribution center information, SKU(Item number) ingestion, First mile, Middle mile and Last mile data ingestion for multiple markets, associate labor wages, associate demand signal processing. Once the data is ingested, performing analytics of data to improve business processes and building dashboards so floor managers(users) can consume this data and perform actionable insights and changes to the business process. 
 
Here are some ways Walmart makes use of this data: 
 
–Inventory management: Identify which product categories are selling well and which ones are not, which can help them make informed decisions about inventory levels. 
–Forecasting: Make more accurate forecasts about future demand for products, which can help manage the inventory more effectively. 
–Lead time optimization: Analyze the trends in lead times for products and optimize their ordering and fulfillment processes to reduce lead times and improve efficiency. 
–Order optimization: Collect patterns in customer behavior that indicate when a customer is likely to place an order and optimize the ordering process accordingly by using this demand signal. 
–Real-time inventory tracking: Track inventory levels and order products when they are running low, to minimize stockouts and missed sales opportunities. 
–Warehouse Management: Analyze and identify inventory storage and handling issues and take steps to improve warehouse management, including identifying areas for automation, redesigning the warehouse layout, or investing in new technologies such as robotics. 
 
Have immense experience in Technology, business stakeholder management and soft skills and 360 degree of all functions associated to technology including Sales, Marketing, Finance, Legal and HR 
 
 
JIRA, Rally, Visual Studio Team Services, ETL, EDW, Informatica PowerCenter, 
Business Intelligence, Data Warehousing, Data Science, Tableau, Balsamiq Mockups, 
Axure RP, Pega, AWS, API, HTML, CSS, Java, JavaScript, SQL, iOS, Android, Mobile 
Product Management, HP ALM, Slack, User Stories, Wireframes, Storyboarding, People 
Management, Customer Understanding, A/B testing, B2B SaaS API and Data Product, 
Requirements Gathering, Payments, User Experience – UI/UX, Omniture, Mobile 
Applications, Product Roadmaps, Prioritization, eCommerce, Travel, Hospitality, SAFe 
Agile.