Offers “Amazon”

Filled Amazon

2020 Research Science Intern

  • Internship
  • Turin (Torino)
  • IT development

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Job description



DESCRIPTION

As a Research Scientist intern, you will be responsible for data-driven improvements to our models. Regardless of the team you join, your work will directly impact our customers.

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

PREFERRED QUALIFICATIONS

· Track record of diving into data to discover hidden patterns and of conducting error/deviation analysis
· Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
· The motivation to achieve results in a fast-paced environment.
· Experience with statistical modelling / machine learning
· Strong attention to detail
· Exceptional level of organization
· Comfortable working in a fast paced, highly collaborative, dynamic work environment
· Ability to think creatively and solve problems
· Fluency in a foreign language

Amazon is an Equal Opportunity Affirmative Action Employer - F/M/V/D

Ideal candidate profile



BASIC QUALIFICATIONS

· Pursuing a Master’s or PhD in a relevant field
· Experience in Perl, Python, or another scripting language; command line usage
· Experience with various machine learning techniques and parameters that affect their performance
· Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.