FAQs
What qualifications are required for the Applied Scientist position at Audible?
Candidates must have an MS in a relevant quantitative field with 5 years of experience or a PhD.
What programming languages should I be proficient in for this role?
Fluency in Python and SQL is required, along with skills in Java, C++, or other programming languages.
What kind of experience is needed in machine learning for this position?
Candidates should have depth and breadth in state-of-the-art machine learning technologies, experience in algorithm development, and machine learning pipeline orchestration with AWS or similar cloud platforms.
Is domain knowledge required for this role?
While it's preferred, candidates with domain knowledge of comparable products (digital, retail) will be favored but not strictly required.
Are there opportunities for innovation in this role?
Yes, the role encourages inventing groundbreaking fraud detection and mitigation solutions, allowing for a proven track record of innovation in creating novel algorithms.
Will I be collaborating with other teams at Audible?
Yes, you will work closely with other data scientists, ML experts, engineers, and business teams across the globe on cross-disciplinary efforts.
What are the primary responsibilities of the Applied Scientist?
Responsibilities include developing fraud detection solutions, creating data engineering and modeling pipelines, and translating fraud patterns into actionable insights.
Are publications in top-tier conferences or journals required for this role?
While not required, having publications in top-tier peer-reviewed conferences or journals related to NLP, deep learning, or machine learning is preferred.
How does Audible support diversity in its hiring process?
Audible is committed to diversity and makes recruiting decisions based on candidates' experience and skills, valuing passion and the ability to discover and invent.
What kind of work environment can I expect at Audible?
Audible offers an agile environment that supports collaboration on research, design, and model development throughout various projects.