Applied Scientist

Audible; Newark (NJ); On-site; USD 140,000 - 210,000; Full time

Job summary

Audible is seeking an Applied Scientist to build ML/NLP models that detect fraud and safeguard customers and creators. You will design scalable data pipelines, prototype novel defenses, and translate patterns into actionable insights for risk management.

You'll work with data scientists, ML engineers, and business partners across the globe in an agile environment to ship end-to-end fraud protection solutions and advance our defense capabilities.

Qualifications

• MS or PhD in a quantitative field with 5+ years' relevant experience.
• Fluent in SQL and Python.
• Deep expertise in ML technologies and NLP.
• Experience building ML pipelines on AWS SageMaker and related services.
• Big data engineering with Spark, EMR, and Glue.
• Proven track record in Agile software development.

Responsibilities

• Protect Audible customers and content from AI-generated fraud.
• Develop scalable data engineering and modeling pipelines.
• Invent novel fraud detection and mitigation solutions.
• Collaborate with data scientists, engineers, and business teams globally.
• Contribute ideas and learning to grow the Audible Data Science team.

Skills

SQL

Python

Machine Learning

NLP

Fraud detection

Education

MS/PhD in CS or quantitative field

Tools

SageMaker

Spark / EMR

Glue

Lambda/Step Functions

Job description

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

In this role, you'll employ scalable 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 an 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 novel fraud detection and mitigation solutions using innovative 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.

• Experience creating novel algorithms and advancing the state of the art
• 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
• Fluent in SQL, Python
• Depth and breadth in novel 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
• Experience with Agile Software Development