Summarizing my last 11+years at Amazon, I have played a pivotal leadership role in driving Applied Machine Learning (ML) & AI strategy and spearheading critical ML initiative & projects across various organizations in Amazon Selling Partner Services (SPS) and Alexa businesses, leading to innovation and launches with multi-million dollar returns, impacting all locales of world-wide Amazon operations. In SPS business, my initiatives aimed at improving Amazon product discovery, engagement, and conversion for both internal and external brands and helping Selling Partners grow their business on Amazon. In Alexa, I have spearheaded and delivered premium customer experiences esp. for Kids, content optimization, intelligent sales and Alexa Ads solutions. Some of my efforts have resulted in incrementality measurement tools and marketing action mix implementations, driving significant Free Cash Flow (FCF) and establishing standardized measurement frameworks across Amazon. On applied sciences front front, I have established teams that successfully research into applying Generative AI, Causal Inference and Estimation, Recommender Systems, SysML, Natural language processing and Understanding, Forecasting, and more sub fields of ML/AI. Some of these initiatives have redefined industry standards and how customers shop and engage with AI technology in their day-to-day life.

In Selling Partner Service (erstwhile 3P marketplace) business, I initiated and led many efforts that have resulted in building Amazon wide platforms supporting incrementality measurement tools, marketing action mix recommendations (primarily Seller Promotions like Deals, Coupons and other discounts, support big event days like Prime Day, Black Friday, Cyber Monday etc.) to grow sellers and brands on amazon, which have driven $100s of millions of dollars of Free Cash Flow (FCF) and establishing standardized measurement frameworks across Amazon. I also spearheaded building science competencies for Alexa Experience and Revenue Optimization, Alexa Ads and Alexa Kids & Monetization organizations ground up and successfully leading them.

Was the first Data Scientist of the organization and led ML/AI/Data effort in collaboration with product managers to drive the growth of third-party sellers within Amazon Marketplace. Played a crucial role in developing and scaling the Seller Recommendations program, leveraging advanced data pipelines and predictive models to deliver personalized, actionable recommendations across various areas, including pricing optimization, inventory management, product listing enhancements, advertising strategies, and fulfillment optimization, directly contributing to Amazon’s 2016 Net sales $136.0 billion and operating income of $4.2 Billion.

As a Data engineer in the organization, I helped creation of a large-scale data ecosystem, and re-usable feature repositories and data marts, utilizing petabytes of data and distributed processing to support ML/AI initiatives across Amazon. My organization spearheaded the development of Amazon wide large scale distributed data and ML platform to support company wide distributed ETL and Machine Learning needs pioneering the advancement of large scale Hadoop and Spark based data/ML development.

I played a pivotal role in several transformative projects, leveraging my strength in Business Intelligence (BI) and Data Warehouse (DWH) systems, building foundational tech aiding all stages of analytics (descriptive, diagnostic, predictive, prescriptive) and downstream applied Machine Learning. My contributions included designing and implementing the offline Data Warehousing using Dimensional Modeling, automating ETL processes for downstream ML modeling and developing comprehensive BI dashboards. Additionally, I collaborated closely with stakeholders to refine business processes and ensure successful change management, all of which were instrumental in enhancing sales intelligence and operational efficiency of Oracle University (OU) business bringing holistic innovation.

During my tenure at Keane, I made Significant contributions by designing and developing domain specific Data Marts, optimizing existing database structures, performance tuning initiatives using various database technologies for clients like McKesson (USA), Philips - Lifeline (USA), UNISYS (Belgium) and STAPLES (USA). These initiatives were stepping stones for the clients in their journey in building predictive intelligence using their enterprise data aiding efficient decision making. Was part of the Database practice and Information Management COE under the Architecture Solutions Group (ASG).