AMAZON Data Science Internship Program: Applications are welcomed from motivated data scientists with good leadership skills and the capacity to create, automate, and operate analytical models of systems.
This Amazon internship opportunity is highly competitive and is available to eligible candidates currently enrolled in Master’s or Ph.D. degree in math, statistics, computer science, or related science field
Company/Organization(s): Amazon
Program Type: Graduate Students Internship
Program Location: Munich, Germany
Program Duration: Not specified
Available Slot: Not specified
Basic Qualifications: M.Sc or PhD Students
Nationality: All Nationalities
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AMAZON Data Science Internship Program | What to Expect
Amazon is an equal opportunities employer. Believe passionately that employing a diverse workforce is central to success. The company makes recruiting decisions based on your experience and skills:
- Experience in machine-learning methodologies (e.g., supervised and unsupervised learning, deep learning, etc.)
- Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive).
- Experience in communicating technically, at a level appropriate for the audience.
Eligible Criteria
Applicants for AMAZON Data Science Internship Program are expected to meet the following criteria for this opportunity:
- Enrolled in Master’s or Ph.D. degree in math, statistics, computer science, or related science field.
- Experience with regression modeling, forecasting, and time series analysis.
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or
statistical/mathematical software (e.g. R, SAS, or Matlab) - Experience using one or more programming languages (e.g., Python, Java, C++, C, etc.).
- Experience with big data: processing, filtering, and presenting large quantities (100K to Millions of rows) of data.
- Applicants have a demonstrated ability to manage medium-scale modeling projects, identify requirements, and build methodology and tools that are statistically grounded.
- You will have experience collaborating across organizational boundaries.
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Application Procedures
How-to-Apply: Interested and qualified candidates for the AMAZON Data Science Internship Program can proceed to the online application portal to submit their application.
Application Deadline: Not Specified
Visit the Official Website for Further Details
AMAZON Data Science Internship Program | What to Expect
Amazon is an equal opportunities employer. Believe passionately that employing a diverse workforce is central to success. The company makes recruiting decisions based on your experience and skills:
- Experience in machine-learning methodologies (e.g., supervised and unsupervised learning, deep learning, etc.)
- Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive).
- Experience in communicating technically, at a level appropriate for the audience.
Eligible Criteria
Applicants for AMAZON Data Science Internship Program are expected to meet the following criteria for this opportunity:
- Enrolled in Master’s or Ph.D. degree in math, statistics, computer science, or related science field.
- Experience with regression modeling, forecasting, and time series analysis.
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or
statistical/mathematical software (e.g. R, SAS, or Matlab) - Experience using one or more programming languages (e.g., Python, Java, C++, C, etc.).
- Experience with big data: processing, filtering, and presenting large quantities (100K to Millions of rows) of data.
- Applicants have a demonstrated ability to manage medium-scale modeling projects, identify requirements, and build methodology and tools that are statistically grounded.
- You will have experience collaborating across organizational boundaries.
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Application Procedures
How-to-Apply: Interested and qualified candidates for the AMAZON Data Science Internship Program can proceed to the online application portal to submit their application.
Application Deadline: Not Specified
Visit the Official Website for Further Details
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