Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. The AB Sales Analytics, Data, Product and Tech (ADAPTech) team uses CRM, data, product, and science to improve Sales productivity and performance. It has four pillars: 1) SalesTech maintains Salesforce to enable Sales workflows, and supports >2K users in nine countries; 2) Product and Science builds tools embedded with bespoke Machine Learning (ML) and GenAI large language models to enable sales reps to prioritize top accounts, position the right Amazon Business (AB) product features, and take actions based on critical customer events; 3) Sales Data Management (SDM) and Sales Account Management (SAM) enrich customer profiles and business hierarchies while improving productivity through automation and integration of internal/external tools; and 4) Business Intelligence (BI) enables self-service reporting simplifying access to key insights through WBRs and dashboards. Sales teams leverage these products to identify which customers to target, what features to target them with, and when to target them, in order to capture their share of wallet. A successful Applied Scientist at Amazon demonstrates bias for action and operates in a startup environment, with outstanding leadership skills, and proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We need great leaders to think big and design new solutions to solve complex problems using machine learning (ML) and Generative AI techniques to improve our customers’ experience when using AB. You have hands-on experience making the right decisions about technology, models and methodology choices. Key job responsibilities As an Applied Scientist, you will primarily leverage machine learning techniques and generative AI to outreach customers based on their life cycle stage, behavioral patterns, and purchase history. You may also perform text mining and insight analysis of real-time customer conversations and make the model learn and recommend the solutions. Your work will directly impact the trust customers place in Amazon Business. You will partner with product management and technical leadership to identify opportunities to innovate customer journey experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but also play a crucial role in shaping strategies. Additional responsibilities include: -Design, implement, test, deploy and maintain innovative data and machine learning solutions to further the customer experience. -Create experiments and prototype implementations of new learning algorithms and prediction techniques -Develop algorithms for new capabilities and trace decisions in the data and assess how proposed changes could potentially impact business metrics to cater needs of Amazon Business Sales -Build models that measure incremental value, predict growth, define and conduct experiments to optimize engagement of AB customers, and communicate insights and recommendations to product, sales, and finance partners. A day in the life In this role, you will be a technical expert with significant scope and impact. You will work with Technical Product Managers, Data Engineers, other Scientists, and Salesforce developers, to build new and enhance existing ML models to optimize customer experience. You will prototype and test new ideas, iterate quickly, and deploy models to production. Also, you will conduct in-depth data analysis and feature engineering to build robust ML models.
