Description

　　Have you ever wanted to solve a mystery or be part of solving a case? Are you fascinated by detective stories or crime shows on TV? Do you love to catch bad actors, build ML models and solve complex problems. If so, working on the Loss Prevention Tech team as a Sr Applied Scientist is the place for you!

　　We detect theft, fraud and organized crime happening across our global supply chain and operations for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the cost savings of Amazon's inventory losses.

　　The primary role of a Sr Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on all the fraud investigations happening across Amazon operations.

　　Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in ( Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of building fraud detections, detecting organized crime and the ability to use data and research to make changes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment.

　　Key job responsibilities

　　Own KPIs that measure fraud management performance and efficiencies.

　　Detect and automate theft, fraud MOs

　　Detect organized crime rings and bad actor clusters

　　Build data or computer vision based ML models

　　Perform end to end evaluation of operational defects, system gaps, and scaling challenges (both system and operational).

　　Contribute to the overall fraud management and product development strategies.

　　Present key learnings and vision to stakeholders and leadership.

　　Integrate ML detection models via software applications

　　About the team

　　We believe that building a culture that is welcoming and inclusive is integral to people doing their best work and is essential to what we can achieve as a company. We actively recruit people from diverse backgrounds to build a supportive and inclusive workplace. Our team puts a high value on work-live balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment.

　　We are open to hiring candidates to work out of one of the following locations:

　　Seattle, WA, USA

　　Basic Qualifications

　　PhD, or Master's degree and 6+ years of applied research experience

　　5+ years of building machine learning models for business application experience

　　Knowledge of programming languages such as C/C++, Python, Java or Perl

　　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.

　　Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

　　Experienced in using multiple data science methodologies to solve complex business problems.

　　Experienced in handling large data sets using SQL and databases in a business environment.

　　Excellent verbal and written communication.

　　Experience in fraud detection or criminal investigations

　　Thrive in a fast-paced, innovative environment.

　　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 $136,000/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. Applicants should apply via our internal or external career site.