Applied Scientist

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners.

We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other.

Imagine your future with us.

ABOUT THIS ROLE

Ensure you read the information regarding this opportunity thoroughly before making an application.

In this role, you'll employ scalable cutting-edge machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms.

You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs.

Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems.

You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management.

You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects.

As a Senior Applied Scientist, you will :

• Protect Audible’s customers and content creators against the onslaught of AI-generated fraud
• Develop Amazon-scale data engineering & modeling pipelines
• Imagine and invent before the business asks, and create groundbreaking fraud detection and mitigation solutions using cutting-edge approaches
• Work closely with other data scientists, ML experts, engineers as well as business across the globe, and on cross-disciplinary efforts with other scientists within Amazon
• Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others

ABOUT AUDIBLE

Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers' daily lives.

We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors.

This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

Minimum Requirements :

• MS in one of the following disciplines : Computer Science, Statistics, Data Science, Economics, Applied Math, Operational Research or a related quantitative field +5 yrs relevant experience; or PhD
• Fluency in Python, SQL or similar scripting languages and skilled at Java, C++, or other programming languages
• Experience in algorithm development
• Depth and breadth in state-of-the-art machine learning technologies
• Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
• Big Data Engineering with Spark / AWS EMR & Glue
• Domain knowledge of comparable products (digital, retail)
• Publications at top-tier peer-reviewed conferences or journals in one of those areas (natural language processing / understanding, deep learning, machine learning, or speech processing)
• Proven track record of innovation in creating novel algorithms and advancing the state of the art

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 Amazon Disability Accommodation.

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