Senior Applied Scientist, Amazon Devices, Device Science and Data Technology
DESCRIPTION
The Amazon Devices team designs and engineers high-profile consumer electronics, including the best-selling Kindle family of products. We have also produced groundbreaking devices like Fire tablets, Fire TV, Amazon Dash, and Amazon Echo.
What will you help us create?
The Team: How often have you had an opportunity to be a founding member of a team that is solving a significant problem through innovative technology? Would you like to know more about how we are envisioning the use of machine learning and GenAI to solve these problems? If this sounds intriguing, then we’d like to talk to you about a role on our team that's tackling a set of problems requiring significant innovation and scaling.
As a Sr. Applied Scientist, you will design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, helping Amazon Devices to provide great products. This role will be a key member of a Science and Data technology team based in Denver, CO. You will work closely with other scientists, machine learning experts, and engineers to design and run experiments, research new algorithms, and find new ways to improve Amazon Device Services & Software products. You will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Our scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.
Key job responsibilities
- Define proper output business metrics, and build input models to identify patterns and drivers of the output.
- Drive actions at scale using scientific methods and decision-making
- Design and develop complex mathematical, statistical, Machine Learning, GenAI models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions
- Design experiments, test hypotheses, and build actionable models
- Prototype these models by using modeling languages such as R or in software languages such as Python.
- Work with software engineering teams to drive scalable, real-time implementations
- Utilizing Amazon systems and tools to effectively work with terabytes of data
BASIC QUALIFICATIONS
- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.