DESCRIPTION:
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud?<br><br>Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems?<br><br>Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment?<br><br>If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.<br><br>Key job responsibilities<br>Use machine learning and statistical techniques to create scalable risk management systems<br>Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends<br>Design, development and evaluation of highly innovative models for risk management<br><br>Working closely with software engineering teams to drive real-time model implementations and new feature creations<br><br>Working closely with operations staff to optimize risk management operations,<br><br>Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation<br><br>Tracking general business activity and providing clear, compelling management reporting on a regular basis<br><br>Research and implement novel machine learning and statistical approaches
BASIC QUALIFICATIONS:
- 3+ years of building machine learning models for business application experience<br>- PhD, or Master's degree and 6+ years of applied research experience<br>- Experience programming in Java, C++, Python or related language<br>- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS:
- A PhD in CS, Machine Learning, Statistics, Operations Research or relevant field<br>- 6+ years of industry experience in predictive modeling and analysis<br>- Strong Machine Learning breadth and depth<br>- Strong skills with SQL<br>- Strong skills with Spark/Python/Perl (or similar)<br>- Ability to think creatively and solve problems<br>- Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams<br><br>Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.<br><br>Our 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 <a href="https://amazon.jobs/content/en/how-we-hire/accommodations">https://amazon.jobs/content/en/how-we-hire/accommodations</a> for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.<br><br>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 <a href="https://www.aboutamazon.com/workplace/employee-benefits">https://www.aboutamazon.com/workplace/employee-benefits</a>. This position will remain posted until filled. Applicants should apply via our internal or external career site.