Job Description

At Audible, we believe in the transformative power of stories. Every day, we work to bring the world’s most compelling audio storytelling to millions of global listeners. As an Applied Scientist, you’ll play a critical role in protecting our listeners, creators, and systems by developing state-of-the-art fraud detection and prevention technologies.

Imagine the possibilities of working with Amazon-scale data, cutting-edge machine learning, and deep learning techniques—all while empowering customers to safely enjoy the stories they love. This is your chance to dream big, invent boldly, and make an impact that matters.

What You’ll Do

• Defend Against Emerging Threats: Develop groundbreaking fraud detection and mitigation solutions, staying ahead of AI-generated fraud.
• Leverage Advanced Technologies: Utilize machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) to uncover complex patterns, predict fraudulent behavior, and protect Audible's customers and creators.
• Build Scalable Systems: Design Amazon-scale data engineering and modeling pipelines, implementing solutions using AWS platforms like SageMaker, EMR, and Glue.
• Collaborate Across Disciplines: Work closely with data scientists, engineers, and business teams globally, as well as partner with other Amazon scientists on cross-disciplinary projects.
• Drive Innovation: Contribute your expertise to advance Audible’s fraud defense capabilities, translating intricate fraud patterns into actionable insights that protect our community.

What Makes You a Great Fit

Basic Qualifications:

• Educational Foundation: MS or PhD in Computer Science, Statistics, Data Science, Applied Math, or a related field.
• Technical Expertise:
  • Fluency in Python, SQL, or similar scripting languages.
  • Proficiency in Java, C++, or other programming languages.
  • Experience in algorithm development and machine learning pipelines.
• Cloud Platform Knowledge: Experience with AWS services like SageMaker, Lambda, and Step Functions for ML pipeline orchestration.
• Big Data Mastery: Skilled in Spark and AWS EMR for large-scale data engineering.

Preferred Qualifications:

• Domain expertise in digital or retail products.
• A proven track record of innovation, including publications in top-tier ML, NLP, or AI journals.
• Experience creating novel algorithms and advancing the state of the art in machine learning.