Selling Partner Services (SPS) is the custodian of the selling partners’ journey through Amazon.com and is responsible for several key elements of Amazon’s business. The primary goals of SPS are to drive awareness and motivation to sell among the global entrepreneurs, simplify onboarding and provide education throughout the Selling Partner lifecycle, ensure price competitiveness, and finally, drive product discovery so partners can effectively convey their brand/product story.
Are you excited about driving business growth for millions of sellers through application of Machine Learning and other advanced computer science disciplines? Do you thrive in a fast-moving, large-scale environment that values data-driven decision making and sound scientific practices? We are looking for experienced applied scientists build the next level of intelligence that will help Amazon Marketplace Sellers to succeed and grow their businesses.
Amazon Marketplace enables sellers to put their products in front of hundreds of millions of customers and offers sellers the tools and services needed to make e-commerce successful, efficient and simple. Our team is responsible for building the core intelligence, insights, and algorithms that support a broad range of products and features that Amazon Marketplace Sellers depend on. We are tackling large-scale, challenging problems such as helping sellers to prioritize business tasks, and predicting customer demand for new products, by bringing together petabytes of data from diverse sources across Amazon.
You should have a proven track-record of delivering solutions using advanced computer science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions that impact millions of customers globally, and be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.
Develop production software systems utilizing advanced algorithms to solve business problems.
Analyze and validate data using data, algorithms, and statistical tools to ensure high data quality and reliable insights.
Proactively identify interesting areas for deep dive investigations and future product development.
Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.
Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.
Leverage industry best practices to establish repeatable applied science practices, principles & processes.
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
· MS in computer science, engineering or related field with 3 years of relevant experience.
· Able to understand and translate business and product questions into analytics projects.
· Excellent understanding of computer science fundamentals, data structures, and algorithms.
· Strong programming skills (one or more of Java, C/C++, Python) in industrial setting.
· Great design and problem solving skills, passion for quality and engineering excellence at scale.
· Ability to work independently in a fast-paced, iterative development environment.
· Understanding of relational databases and familiarity with statistics.
· Exemplary communication skills, ability to work with large cross functional teams of technical and non-technical members.
· PhD in computer science, engineering, or related field with 5 years of relevant experience.
· Expertise in specialized areas such as Causal Modeling, Machine Learning, Natural Language Processing, Text Mining, Graph Processing, Search, Recommendation Systems, or Signal Processing.
· Knowledge and direct experience using statistical packages and business intelligence tools.
· Experience in gathering requirements and formulating business metrics for reporting.
· Track record of executive level reporting to that drive business decisions.