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Intern - Data Science

  • Internship
    Full-time
    Summer Internship
  • Boise

AI generated summary

  • You should have a strong desire for a data science career, skills in statistical modeling and machine learning, experience with SQL, Python/R, and software development, and be pursuing a relevant degree.
  • You will analyze diverse datasets, apply machine learning techniques, extract data using SQL, enhance software skills, and communicate findings while collaborating in a manufacturing environment.

Requirements

  • Strong desire to grow a career as a Data Scientist in highly automated industrial manufacturing doing analysis and machine learning on terabytes and petabytes of diverse datasets.
  • Experience in the areas: statistical modeling, feature extraction and analysis, supervised/unsupervised/semi-supervised learning. Exposure to the semiconductor industry is a plus but not a requirement.
  • Ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.
  • Strong software development skills.
  • Strong verbal and written communication skills.
  • Experience with or desire to learn:
  • Machine learning and other advanced analytical methods
  • Fluency in Python and/or R
  • pySpark and/or SparkR and/or SparklyR
  • Hadoop (Hive, Spark, HBase)
  • Teradata and/or another SQL databases
  • Tensorflow, and/or other statistical software including scripting capability for automating analyses
  • SSIS, ETL
  • Javascript, AngularJS 2.0, Tableau
  • Experience working with time-series data, images, semi-supervised learning, and data with frequently changing distributions is a plus
  • Experience working with Manufacturing Execution Systems (MES) is a plus
  • Existing papers from CVPR, NIPS, ICML, KDD, and other key conferences are plus, but this is not a research position
  • Education:
  • B.S. in Mathematics, Computer Science, Data Science and Physics.
  • Current going for M.S. or Ph.D. with specialization in Data analytics, mathematics, Physics, Statistics, computer science.

Responsibilities

  • Strong desire to grow a career as a Data Scientist in highly automated industrial manufacturing doing analysis and machine learning on terabytes and petabytes of diverse datasets.
  • Experience in the areas: statistical modeling, feature extraction and analysis, supervised/unsupervised/semi-supervised learning. Exposure to the semiconductor industry is a plus but not a requirement.
  • Ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.
  • Strong software development skills.
  • Strong verbal and written communication skills.
  • Experience with or desire to learn:
  • Machine learning and other advanced analytical methods
  • Fluency in Python and/or R
  • pySpark and/or SparkR and/or SparklyR
  • Hadoop (Hive, Spark, HBase)
  • Teradata and/or another SQL databases
  • Tensorflow, and/or other statistical software including scripting capability for automating analyses
  • SSIS, ETL
  • Javascript, AngularJS 2.0, Tableau
  • Experience working with time-series data, images, semi-supervised learning, and data with frequently changing distributions is a plus
  • Experience working with Manufacturing Execution Systems (MES) is a plus
  • Existing papers from CVPR, NIPS, ICML, KDD, and other key conferences are plus, but this is not a research position

FAQs

What is the main responsibility of the Data Science Intern?

The main responsibility includes performing analysis and machine learning on large datasets while expressing a strong desire to grow a career as a Data Scientist in a highly automated industrial manufacturing environment.

What qualifications are required for this internship position?

Candidates should have a B.S. in Mathematics, Computer Science, Data Science, or Physics and be currently pursuing an M.S. or Ph.D. with a specialization in Data Analytics, Mathematics, Physics, Statistics, or Computer Science.

Is experience in the semiconductor industry required?

No, experience in the semiconductor industry is a plus but not a requirement for this internship position.

Are there any preferred skills for the Data Science Intern role?

Yes, preferred skills include experience with machine learning, fluency in Python and/or R, SQL query languages, Hadoop, and statistical software like TensorFlow, among others.

What sort of data analysis techniques should candidates be familiar with?

Candidates should be familiar with statistical modeling, feature extraction and analysis, supervised/unsupervised/semi-supervised learning, data cleansing, and outlier identification techniques.

How important are communication skills for this position?

Strong verbal and written communication skills are very important for the Data Science Intern position.

Does Micron provide benefits to interns?

Yes, Micron is dedicated to personal wellbeing and professional growth, offering benefits such as medical, dental, vision plans, and paid time off.

Does this position involve research work?

No, while existing papers from key conferences are a plus, this is not a research position.

How can candidates apply for this internship?

Candidates can apply by visiting Micron's careers page at micron.com/careers.

Does Micron have a policy against discrimination?

Yes, Micron is an equal opportunity workplace and an affirmative action employer, ensuring all qualified applicants receive consideration for employment without regard to various factors including race, gender, and disability.

We're accelerating the transformation of information to intelligence.

Manufacturing & Electronics
Industry
1-10
Employees
1978
Founded Year

Mission & Purpose

Micron is a world leader in innovative memory solutions that transform how the world uses information. We have approximately 40,000 team members in 17 countries who work with the world’s most trusted brands, delivering memory and storage systems for a broad range of applications and sparking countless possibilities in technology.