Research Scientist

Job description

DESCRIPTION

Alexa is the groundbreaking, cloud-based intelligent agent that powers Echo and other devices designed around your voice. The Alexa Music team is building the science and technology behind Alexa. Come join us! Our goal is to delight our customers by adding new music features to Alexa and by improving the accuracy of our existing speech recognition and natural language processing systems for music related use cases.

As a Research Scientist, you will be responsible for the natural language understanding models for our featured music domain. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology.

You will:
· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.
· Clean, analyze and select data to achieve goals
· Build and release models that elevate the customer experience and track impact over time
· Collaborate with colleagues from science, engineering and business backgrounds.
· Present proposals and results in a clear manner backed by data and coupled with actionable conclusions
· Work with engineers to develop efficient data querying infrastructure for both offline and online use cases

Desired profile

BASIC QUALIFICATIONS

· Master's or PhD in a relevant field
· 3+ years' experience with various data analysis and visualization tools
· Experience using Python, Perl, or another scripting language; command line usage
· Experience diving into data to discover hidden patterns and conducting error analysis
· Experience applying various machine learning techniques and understanding the key parameters that affect their performance
· Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationships
· Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.