Data Scientist, AWS Fraud Prevention

Key Skills

Java Programming, Risk Analytics, Python Programming, SQL, AWS, Big Data Technology, Machine learning techniques, Amazon RedShift, Amazon Elastic Compute Cloud-EC2, Amazon RDS, AWS Sagemaker, DynamoDB, Lambda, Amazon Simple Storage Service (S3), Statistical modeling, Data pipelines, ETL frameworks, Mathematics, Data Analysis

Job Description

AWS has the most services and more features within those services, than any other cloud provider - from infrastructure technologies like compute, storage, and databases - to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things.

The AWS Platform team sustains over 750 million transactions per second and is always innovating with the customer in mind.

Would you like to join the team that protects the global AWS platform from fraud? Do you enjoy thinking like a fraudster and using your technical skills to help detect & mitigate AWS accounts from being compromised? If so, AWS Fraud Prevention has an exciting opportunity for you.

As a Data Scientist, you will work directly with Business Analysts and Software Development Engineers to monitor the flavor/ trend of compromise on AWS worldwide and design appropriate solutions to respond in a collaborative environment. There are no walls, and success is determined by your ability to dive deep, and understand the subtle demands new and complex services will place upon systems and teams.

Key job responsibilities

• Apply state-of-the-art Machine Learning methods to large amounts of data from different sources to build and productionalize fraud prevention, detection and mitigation solutions
• Dive deep into the problems using SQL and scripting languages like Python/R to drive short term and long term solutions leveraging Statistical Analysis
• Analyze data (past customer behavior, sales inputs, and other sources) to figure out trends, create compromise prevention and mitigation solutions and output reports with clear recommendations
• Collaborate closely with the development team to recommend and build data-driven innovations
• Manage your own process: identify and execute on high impact projects, triage external requests, and make sure you bring projects to conclusion in time for the results to be useful
• Providing on-call product support approximately once every 3 months

About the team

The AWS Fraud Prevention Compromise vertical is responsible for detecting & mitigating AWS account compromise. You’ll be part of a team of Data Scientists, Investigations Analysts, and Technical & non-Technical Program Managers. The team’s goal is to identify and neutralize fraudsters from compromising AWS customers’ accounts.

Mentorship and Career Growth

Our team is dedicated to supporting new team members. Our team has a broad mix of experience levels and Amazon tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Amazonians truly enjoy mentoring and on-boarding new team members through one-on-one mentoring and thorough, but kind, code reviews.

Work-Life Balance

Our team 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.

BASIC QUALIFICATIONS

• Master’s or Bachelor’s degree in Mathematics, Statistics, Computer Science or in another related field
• Hands-on relevant experience using programming/scripting languages such as Python or equivalent
• Proven understanding of Statistical Analysis, Modeling and Machine Learning techniques
• Experience in designing and deploying ML modeling and prediction pipelines
• Ability to leverage SQL or Spark for Ad-hoc analyses and building out ETL pipelines on heterogeneous data sources

PREFERRED QUALIFICATIONS

• Experience and proficiency with AWS technologies (EC2, CloudTrail, S3, SageMaker, Lambda, DynamoDB, RDS, etc.), and Big Data technologies
• Familiarity with AWS Redshift, Spark or other distributed computing technologies
• Previous work as a Data Scientist in the context of fraud analytics or risk scoring
• Ability to work in a fast-paced, ambiguous environment while prioritizing and managing multiple responsibilities