Skip to main content

2022 Data Science Intern

Job ID: 1662950 | Amazon.com Services LLC

DESCRIPTION

We are looking for motivated data scientists with excellent leadership skills, and the ability to develop, automate, and run analytical models of our systems. You will have strong modeling skills and are comfortable owning data and working from concept through to execution. This role will also build tools and support structures needed to analyze data, dive deep into data to resolve root cause of systems errors and changes, and present findings to business partners to drive improvements.

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.

Amazon has positions available for Data Scientists in multiple locations across the US and Canada.

Amazon is committed to a diverse and inclusive workforce. Amazon is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

BASIC QUALIFICATIONS

· Enrolled in a Master's degree or equivalent in math, statistics, computer science, or related science field.
· Experience with regression modeling, prediction, 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.

PREFERRED QUALIFICATIONS

· Enrolled in a PhD degree or equivalent in math, statistics, computer science, or related science field.
· 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.