Our vision is that Alexa will be the world's most knowledgeable product expert who knows you, in a store that sells everything. All those moments when we need or want to know more about the products we use every day, will have instant satisfaction of an answer: just ask Alexa. We are building a digital product expert that is always available, with super-human knowledge of every product ever made.
We are seeking a Data Scientist to build our Product Information Engine. This is a blue-sky role that gives you a chance to roll up your sleeves and dive into big data sets in order to build simulations and experimentation systems at scale, build optimization algorithms and leverage cutting-edge technologies across Amazon. This is an opportunity to think big about how to understand our customer's needs and solve challenging problems for them.
You will work closely with product and technical leaders throughout Alexa Shopping and will be responsible for influencing technical decisions in areas of development/modelling that you identify as critical future product offerings. You will ensure ease of data-driven decision making for the team and, and build programs to raise the bar in predicting and tuning outcomes for customers.
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
· Bachelor's Degree
· 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· 2 years working as a Data Scientist
· Master's degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with at least 4+ years of working experience as a Data Scientist.
· Experienced in writing science papers for presenting both the methodologies and insights of data explorations.