Akshay Keshri Email and Phone Number

✔ I am currently involved in the development of PriceSmart, a tool that is designed with advanced and complex features similar to Marksmart, but with additional features that cater to various client requirements. It is a generic version that can handle all use cases we have seen across various implementations of Marksmart.As a major stakeholder in this project, I am responsible for writing user stories, designing screens, and creating roadmaps and sprint plans. Working closely with other core team members, I assess feasibility and establish timelines for different versions of the tool and oversee their deployment.In addition to my role as a stakeholder, I am also a hands-on contributor to the product and optimization team, where I help to formulate logic and database schemas for the tool. By leveraging my expertise and collaborating closely with other team members, I am confident that we will be able to create a highly effective pricing tool that delivers significant value to our clients.

◼ Contributed to building and designing the Analytical aspect of the web-based Assort Smart Tool, a Generic Assortment planning Tool. The tool aims to breaks down the budget from high to granular level and allocates it at the store and style color level. It features dynamic elements such as store clustering, product attribute clustering, product mapping, and executive summary for budget splits. It uses an optimization algorithm to allocate units and assort them based on ST, Depth, and Margin metrics. The tool has been deployed successfully and is used by various fashion retailers.◼ As a leader in the development of the MarkSmart optimization module, I worked closely with users, analysts, and PMs to identify the dimensions and constraints of the platform. Using my expertise in optimization, my team designed an engine with linear equations on Gurobi solver, and collaborated with the development team to integrate and scale the solution based on usage load.The MarkSmart optimizer has been instrumental in improving gross margin rates by 10%, with an overall impact of 25M across users per quarter. Currently, I am working on expanding the feature pipeline to include a scenario builder, hierarchy ruleset, and improving optimization run time. Additionally, I am leading the effort to build the optimization module as a separate service to onboard more users.My passion for creating innovative and effective solutions in AI-based pricing recommendations has driven my success in developing and leading the MarkSmart optimization module. I am excited to continue pushing the boundaries of optimization technology to create maximum value for our clients.

◼ Contributed to reducing churn for a restaurant marketing company by identifying drivers of churn and developing actionable measures. Used internal and external data to cluster restaurants and conducted sentiment analysis of customer feedback. Segregated churn into controllable and uncontrollable factors and derived insights to reduce controllable churn from 3% to 1.2% per month. Developed Tableau dashboard to easily represent reasons and courses of action for the client.◼ Developed and built the web-based buy optimization product Buy Smart for a US fashion retailer that maximizes revenue by optimizing the buying of products from vendors across stores. The product uses MILP model for purchase optimization and store allocation .CNN for image mapping to replace last year's styles with this year's buy and find similar articles.◼ Developed customer segmentation algorithm using customer data to analyze shopping behavior for a major fashion retailer in the US. Implemented affinity and cannibalization techniques to increase product reachability and provided customized promotions and coupons for targeted customer segments, resulting in increased sales.◼ Worked on a recommendation engine for the largest US music retailer to improve their website's user experience. The engine utilized affinity and similarity mapping to suggest related products to customers, while a price elasticity model recommended optimal markdowns. The team also optimized the display order of products to maintain inventory movement and minimize cannibalization.

◼ As a key contributor, I played a vital role in developing an Inventory Replenishment Tool (IRT) and a Media Market Mix (MMM) model for a fast-fashion jeweler in the US & Canada with over $2B in revenue. These tools provided the company with better inventory positioning and optimized spending across different channels and stores.◽ IRT:- The highlight of this tool was its complex forecasting ability, which utilized a demand model to recommend weeks of supply while factoring in demand, promotional effect, seasonality, velocity of sales, safety stock, and inventory status. I also developed an analytical model to minimize lost sales resulting from understocking and a velocity ABC classification model to distinguish Key Value Category (KVC), Key Value Item (KVI) products.◽ MMM:- I developed a model to understand the impact of marketing campaigns and national spending on sales and traffic across various channels. This was presented through an interactive Tableau dashboard, which was integrated with a database to provide daily updates. Additionally, I created a scoring module to allocate budget spend in an optimized way across franchisee stores and various media channels.As a primary stakeholder, I played a crucial role in implementing and delivering the client requirements, keeping them updated throughout the development and support cycles.◼ Contributed to the development of an end-to-end Excel and text scanning tool for a leading bookseller with the largest number of retail outlets. Specifically, my contributions included developing a text mining algorithm to extract and find information about open bids for school materials and expiry. Additionally, I built an Excel tool to view this detailed information and facilitate easy access to information for the client. Overall, this tool significantly streamlined the process of gathering and accessing crucial information, improving the client's overall efficiency and effectiveness in managing bids and school materials.

◼ During my professional journey, I dedicated significant effort to master a range of technical skills that are integral to the field of data science. I invested my time and energy to hone my expertise in tools such as R, Python, SQL, Excel, VBA, and PowerPoint. Through my dedication and hard work, I was able to build a strong foundation in these technical areas.◼ One of the highlights of my career was working closely on a data science project, which allowed me to apply my technical knowledge in a practical setting. Using real client data sets, I was able to sharpen my skills and gain valuable insights from the data. As a result of this experience, I was able to further develop my technical abilities and gain the confidence needed to kick start my career in data science.