Research Scientist - Data Science team - Amazon - Berlin - Wizbii

Research Scientist - Data Science team

  • Durch Amazon
  • Berlin (Deutschland)
  • Informationssysteme
2018-08-12T08:49:11+0000

Job description

DESCRIPTION

Interested in Amazon Echo? Come work on it. We're building the speech and language solutions behind Amazon Alexa and other Amazon products and services. Come join us!

As a Data Scientist in our Applied Modeling and Data Science team, you will be responsible for data-driven improvements and evaluation for our spoken language understanding models. Your work will directly impact our customers in the form of 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

Seeked profile

BASIC QUALIFICATIONS

· Bachelor's or Master's degree in a relevant field
· 7+ years experience with various data analysis and visualization tools
· Experience in Perl, Python, or another scripting language; command line usage
· Track record of diving into data to discover hidden patterns and of conducting error/deviation analysis
· Knowledge of various machine learning techniques and key parameters that affect their performance
· Experience developing experimental and analytic plans for data modeling processes, using strong baselines, and determining cause and effect relations
· Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.

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