Greater Chicago Area
Data & AI Executive | 15+ Years Global Leadership | Consistent track record of delivering scalable analytics, science and AI products with measurable ROI through cross-functional collaboration and strategic technical oversight
Responsible for the strategy and value delivery from data science areas covering all business growth areas including supply chain, merchandising, retail operations and ecommerce fulfillment Spearheaded Data Science and AI Strategy for the Source-to-Table Transformation, aligning technical roadmaps with corporate objectives to drive end-to-end operational efficiency. Orchestrate a global team of 40+ Data Engineers, Scientists and ML Engineers across US and Bangalore offices, delivering high-impact Agentic Forecasting solutions that transformed core business processes resulting in 5% improvement in replenishment forecast accuracy Direct full-lifecycle strategic planning, including AOP development and budget management, while presenting key sciences and AI based initiatives to the Steering Committee to secure executive buy-in to enable 300M+ sales growth via better in stock and 150M+ savings via unproductive inventory reduction by achieving and tightly integrating more intelligent ordering, inventory and forecasting solutions in company’s day-to-day store and supply chain operations
Managed data science for supply chain and retail operations domains at Albertsons Companies. Built the team from one data scientist to 20+ high performing data scientists Developed and scaled Intelligent Allocation solution to optimize allocation of constrained inventory at DCs to stores based on days of supply and worked cross functionally with replenishment, retail ops, ML Ops and supply chain IT on implementation and roll out. The solution has helped the company grow sales by $10M+ in 2022 through lost sales reduction. Created data science strategy for improving demand forecasting used in company’s replenishment system and perpetual inventory data quality. Developed and scaled Probabilistic Inventory solution for each item, store combination detecting anomalies in store inventory data using historical sales, shipments, warehouse outs and item velocities and alerting stores to correct them. The solution has reduced lost sales by $60M in year 2024. Created strategic vision for End-to-End Customer Demand Forecasting with executions at store level forecasting for store and DC replenishment and at category level for financial projections Developed data scientists within my own team and in the data science department by helping them through best practices and standards for soft and technical skills.
• Managed enterprise data science team (weekly 1x1s, quarterly appraisals, career development), prioritized data science projects, developed long-term strategy for the company and built relations with different business units to maintain a steady pipeline of data science projects • Implemented driver based ML forecasting models and algorithms to create shipment forecast for hundreds of thousands of SKU/location/customer combinations in o9 platform. Managed a team of a dozen data scientists (internal, external) in ideation, development and testing of the models. Proposed leadership future enhancements. • Led promotional forecasting initiative by guiding my team on feature engineering, machine learning algorithm development, validations and results communication. The forecasting algorithm achieved 90% consumption forecast accuracy for a major retailer at item/week level. • Directed development of price elasticity model for the items in food and beverages categories taking competitor prices and promotions into account. The price elasticities from the model have been provided for over 80 categories at Walmart and FDMX markets for business to simulate impact due to price changes. • Worked with my team to automate SAP’s time-series forecasting models in Python (achieving 6 hours of run time to model entire business) and built optimization models redeploying inventory in the network to minimize supply chain losses/waste and maximize service
● Interviewed 50+ buyers from different business units to understand merchandising pricing decisions and the opportunities for using predictive outcomes ● Productionalized a machine learning solution that estimates how much an item’s unit sales would change based on a proposed price change. This feature provides lift estimates for over 90% of all items across the total box. ● Collaborated with internal design team in creating a clickable prototype for a cannibalization solution that would help buyers see the impact of their pricing decisions on the substitute items enabling total category P&L impact.
● Led a team of data scientists and big data engineers in developing a scalable algorithm, so-called Customer Choice Model (CCM), using observed out-of stocks for transfer of demand and incrementality problem. The productionalized solution has been used by business over 150+ times/month on average with significant impact (for example, just for canned vegetables category, it secured $1M in cost reductions during supplier negotiations and drove a 5.8% increase in sales even though 23% of PODs were deleted). ● Led efforts on creating a new hire on-boarding Confluence site and helped developing talent and building standards in Walmart Bentonville Data Science organization ● Helped with data science recruiting efforts in interviewing for multiple data scientist positions across the data science organization ● Led a team of 15+ data scientists and analysts from teams in Bangalore and Bentonville offices on delivering customer analytics solutions to the merchandising organization o Provided actionable insights to merchants on shopping behaviors including loyalty and substitution, how their items interact with each other (Customer Decision Trees) and opportunities and gaps with Private Bands (100+ food and consumables categories) o Created self-serving solutions in Alteryx for business users to get to the customer data and analytics on their own (cutting the delivery time from 1 week to a day)
● Collaborated with business partners from different parts of the organization (sourcing, global logistics, transportation) on helping them better making decisions resulting in significant cost savings. Established and built relationships with the customers and led the operations research/optimization efforts in the following initiatives. o Bid assignment optimization for ocean lanes (estimated cost savings of $40-50M) o Driver staffing project on optimal allocation of private fleet resources (estimated cost savings of $16M) o Development of a web-based interface to simplify the user’s interaction with the built-in-house optimization solutions o Optimization of next-generation supply chain with focus on last-mile solution for grocery home delivery (critical enabler) o Optimization of outbound shipments from DCs to stores (critical enabler) o Optimal partition of bids on inbound lanes over the planning horizon (estimated cost savings of $2M) ● Provided consultancy to business units (store operations, real estate, marketing, health and wellness) to help their efforts in designing analytical solutions based on quantitative modeling techniques including mathematical programming approaches or heuristics
● Implemented a column generation approach to solve the direct aperture optimization problem ● Improved non-linear optimization algorithms in the production software ● Led the company-wide efforts on developing robust optimization techniques to deliver treatment plans under a range of uncertainties