Why this job is awesome?
· This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery, customer service, and product safety information to every single page on every Amazon-owned site.
· MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.
· We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.
- Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience, product safety, and customer service on every Amazon-owned site?
- Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms 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 one of the Operations Technology Machine Learning teams.
· Research and implement machine learning and statistical techniques to create scalable and effective models in Delivery, Customer Service, and Product Security systems.
· Deep data analysis to solve business problems and to identify business opportunities to provide the best delivery experience on all Amazon-owned sites.
· Design, development and evaluation of highly innovative machine learning models for big data.
· Analyzing and understanding large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities
· Working closely with other software engineering teams to drive real-time model implementations and new feature creations
· Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
· Ability to work on a diverse team or with a diverse range of coworkers.
· Ph.D. in Computer Science, Statistics, Mathematics, Operational Research or in a highly quantitative field.
· 3+ years of hands-on experience in machine learning, predictive modeling and large data analysis which can include Natural Language Processing (NLP), deep learning, recommendation systems, or knowledge graph systems.
· 3+ years Python/R and/or SQL/Hive/Pig experience.
· A Ph.D. in Computer Science, Statistics, Operational Research or in a highly quantitative field.
· 3+ years of industry experience in computer vision, machine learning, Natural Language Processing (NLP), deep learning, recommendation systems, predictive modeling, and large data analysis.
· Strong Python and/or R skills.
· Good familiarity with Perl, Python (or similar scripting language) and Java/C++.
· Familiarity with Hadoop/MapReduce.
· Good communication and presentation skills.
· Excellent problem solving abilities.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us
We believe passionately that employing a diverse workforce is central to our success and we make recruiting decisions based on your experience and skills. We welcome applications from all members of society irrespective of age, gender, disability, sexual orientation, race, religion or belief.