Senior Applied Scientist, Central Machine Learning

This job is with Amazon, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.DESCRIPTION: Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon's customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities- Use machine learning and analytical techniques to create scalable solutions for business problems- Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes- Design, development, evaluate and deploy innovative and highly scalable models for predictive learning- Research and implement novel machine learning and statistical approaches- Work closely with software engineering teams to drive real-time model implementations and new feature creations- Work closely with business owners and operations staff to optimize various business operations- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation- Mentor other scientists and engineers in the use of ML techniquesA day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the International Emerging Stores organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication.About the team Central Machine Learning team works closely with the IES business and engineering teams in building ML solutions that create an impact for Emerging Marketplaces. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on millions of consumers and end users.BASIC QUALIFICATIONS: - PhD, or Master's degree and 6+ years of applied research experience - 8+ years of building machine learning models or developing algorithms for business application experience - Experience with neural deep learning methods and machine learning - 7+ years of building machine learning models for business application experience - Experience working effectively across cross-functional teams and partnering well with people at all levels within an organization PREFERRED QUALIFICATIONS: - Experience with large scale distributed systems such as Hadoop, Spark etc. - Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applicationsOur inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.