Applied Scientist

Amazon Inc.

Omaha (NE)

Vor Ort

USD 172.000 - 223.000

Vollzeit

vor 6 Stunden

Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Benefits dieser Stelle

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Zusammenfassung

Audible is seeking an Applied Scientist to build fraud-detection systems using ML, DL, and NLP. You will scale data pipelines, design predictive models, and protect customers and creators from AI-generated fraud in a fast-paced, agile environment.

You’ll collaborate with data scientists, ML engineers, and business partners worldwide, publish ideas and artifacts, and contribute to enterprise risk management while advancing state-of-the-art fraud defenses.

Qualifikationen

MS in CS or related field with 5+ years of 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

Aufgaben

Protect Audible’s customers and content creators against the onslaught of AI-generated fraud
Develop Amazon-scale data engineering & modelling pipelines
Invent fraud detection and mitigation solutions using innovative approaches
Collaborate with data scientists, ML engineers, engineers and business teams across the globe
Contribute to the growth of the Audible Data Science team by sharing ideas and learnings

Kenntnisse

SQL

Python

Agile Software Development

Machine Learning

AWS SageMaker

Spark

EMR/Glue

Ausbildung

MS in Computer Science

PhD

Tools

SageMaker

Batch

Lambda

Step Functions

Spark

EMR

Glue

Jobbeschreibung

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.

Basic Qualifications

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

Preferred Qualifications

Domain knowledge of comparable products (digital, retail)
Fluency in Java
Experience in developing and enhancing fraud detection and mitigation models and techniques
Knowledge of the latest advancements in AI/ML-powered fraud detection

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Veterans, military spouses, and people with disabilities are encouraged to apply.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits .