Applied Scientist, AWS Fraud Prevention

Key Skills

Java Programming, Python Programming, SQL, MATLAB Programming, R Programming

Job Description

Job summary

Want to stop fraudsters and make a direct multi-million dollar impact to Amazon's bottom line? Come join the AWS Fraud Prevention team and help us fight fraud and financial crimes.

The mission of the Fraud Prevention Team is to keep the AWS platform a safe and trusted place for our customers and partners worldwide and for all services. Fraudsters are continually looking for new ways to steal from companies. We invent new techniques to detect and stop these bad actors, leveraging the latest in modeling (ML, AI) and enforcement.

As an Applied Scientist in AWS, you’ll be leading us in making critical and time sensitive decisions that impact customers. You’ll use your machine learning expertise to build solutions that can scale and solve the business problem, and your engineering experience to build systems that take those solutions to production; it's an exciting opportunity to apply data science to help improve detection accuracy, containment strategy, and ultimate enforcement activity. It’s fast paced, data driven, and impactful.

We are raising the bar for fraud prevention in the Cloud. This is a high impact opportunity, where a successful candidate will have the ability to shape and raise the bar on fraud prevention.

The position requires hands-on expertise in Analytics to identify and isolate issues, Statistical Modeling and traditional Machine Learning, the ability to write queries to aid in data extraction, and the ability to productionalize models.. This role is a self sufficient scientist that can source data, build and evaluate models, and ultimately take those models and rules to deployment. You should have excellent communication skills and be able to work with stakeholders at all levels. Above all you should be a passionate, hard-working and creative person who loves business, loves solving difficult problems and doesn’t mind getting involved in the details.

Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well-balanced life—both in and outside of work.

We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

BASIC QUALIFICATIONS

Qualifications

• PhD or equivalent Master's degree in Machine Learning, Artificial Intelligence, Mathematics, Statistics, Computer Science, Operations Research or in another highly quantitative field
• 3+ years of hands-on industry experience working with large scale ML problems, end to end
• Ability to develop and deploy (in partnership with engineers) Machine Learning models that power specific applications
• 5+ years with Statistical Analysis and Modeling tools like Python, R, Matlab or similar
• Skilled in various Statistical and traditional Machine Learning methods such as tree-based models
• Able to write SQL scripts for analysis and reporting
• Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts
• Enjoys working in a team environment

PREFERRED QUALIFICATIONS

Preferred Qualifications

• 4+ years of applied ML experience in a quantitative filed
• Solid software development experience
• Experience with Natural Language Processing and Deep Learning Models in general
• Knowledge of AWS Infrastructure, Redshift
• Knowledge of AWS services such as EC2, Sagemaker
• Predictive Analytics
• Any Hadoop, NoSQL Database & Technology