Applied Scientist III - Applied Scientist III Applied Scientist III

Title: Applied Scientist III
Location: New York, NY 10001
Duration: 5 months (Onsite)

Daily Schedule: M-F 1st shift with possible OT
Reason for Request: Cover absence of leave
Interview process: 2-part interview process. 1st is an initial call with HM; 2nd part interview as a technical deep dive with members of the team

Job Description:
Applied scientists are specialists with deep expertise in areas such as machine learning, speech recognition, natural language processing, computer vision, knowledge acquisition, etc. They apply scientific principles in their area of expertise and related fields to: (a) assess the feasibility and timeline of new initiatives requiring significant inventions, and (b) develop new algorithms or enhance existing algorithms for significantly improving the customer experience/impact. They are externally aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing the state-of-the-art for improving Amazon's products and services.

Key job responsibilities:

• Develop AI solutions for Music Personalization.
• Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end.
• Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses.
• Effectively communicate technical and non-technical ideas with teammates and stakeholders.
• Stay up to date with advancements and the latest modeling techniques in the field

Must-Have Skills:

• Machine Learning
• Programming
• Cloud Computing Experience

Years of experience:

• Minimum of 10 years preferred 15 years. Equivalent of an L6; Portfolios not required, but will be welcomed

Basic Qualifications

• PhD, or Master's degree and 6 years of applied research experience
• 5 years of building machine learning models for business application experience
• Experience programming in Java, C , Python or related language
• Experience with cloud-based large-scale model deployment
• KPI: Improvement of machine learning model accuracy

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