Research Scientist, Alexa Automotive

Job description

DESCRIPTION

Do you like Alexa? Do you have a passion for cars? Do you love bleeding edge technology? Are you a Research scientist with technical aptitude? Alexa Automotive is looking for a research scientist to bring Alexa - the voice controlled personal assistant behind Amazon Echo – in automobiles. The team seeks to provide seamless experiences for customers in personal mobility with Alexa in the car.

As a Research Scientist, you will be responsible for the natural language understanding models for our customers in local search, navigation/traffic and public transit areas. 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
· 2+ 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.