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Graduate Data Science Intern Jobs in Portland, OR

As an intern, learns and applies knowledge, builds skills, and explores future career opportunities ... Chemistry, Data Science, Math, Statistics, Computer Engineering, or Computer Science Preferred ...

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The intern will assist in building, maintaining, and optimizing Power BI dashboards and reports ... Current junior, senior, or recent graduate in Computer Science, Data Analytics, Statistics ...

The intern will assist in building, maintaining, and optimizing Power BI dashboards and reports ... Current junior, senior, or recent graduate in Computer Science, Data Analytics, Statistics ...

The intern will assist in building, maintaining, and optimizing Power BI dashboards and reports ... Current junior, senior, or recent graduate in Computer Science, Data Analytics, Statistics ...

Identify and organize data and sources of information appropriately assigned. * Summarize ... We understand the scheduling demands of graduate students and will work with you to accommodate ...

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Graduate Data Science Intern information

See Portland, OR salary details

$12

$23

$44

How much do graduate data science intern jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for graduate data science intern in Portland, OR is $23.87, according to ZipRecruiter salary data. Most workers in this role earn between $18.37 and $26.01 per hour, depending on experience, location, and employer.

What does a Graduate Data Science Intern do?

A Graduate Data Science Intern assists data science teams by analyzing large datasets, developing predictive models, and supporting data-driven decision-making. They often work with programming languages like Python or R, and use tools such as SQL, Excel, and machine learning libraries. Interns contribute to real-world projects, gain hands-on experience in data analysis, and help communicate findings to stakeholders. This role serves as a bridge between academic learning and practical application in the workplace.

What types of projects do Graduate Data Science Interns typically work on, and how do these projects support their professional development?

Graduate Data Science Interns often work on real-world data analysis projects such as building predictive models, cleaning and visualizing data, or assisting with machine learning algorithm implementation. These projects are usually part of larger team initiatives and provide interns with hands-on experience using industry-standard tools and methodologies. Collaborating closely with data scientists, engineers, and business stakeholders, interns gain exposure to the end-to-end data science workflow and receive mentorship to support their growth. This experience not only enhances their technical skills but also helps them develop problem-solving and communication abilities crucial for future career advancement.

What are the key skills and qualifications needed to thrive as a Graduate Data Science Intern, and why are they important?

To thrive as a Graduate Data Science Intern, you need foundational knowledge in statistics, machine learning, and data analysis, typically supported by a degree in a quantitative field. Familiarity with programming languages like Python or R, experience with data visualization tools, and understanding of databases are often required. Strong problem-solving abilities, curiosity, and effective communication help interns collaborate and present insights clearly. These skills enable interns to extract actionable information from data and contribute value to team projects.

What is the difference between Graduate Data Science Intern vs Data Analyst Intern?

AspectGraduate Data Science InternData Analyst Intern
Required CredentialsTypically pursuing or recently completed a degree in Data Science, Statistics, or related fieldOften pursuing or recently completed a degree in Data Analysis, Business, or related field
Work EnvironmentResearch-focused, data modeling, machine learning projects, collaborative teamsData collection, cleaning, reporting, visualization tasks
Employer & Industry UsageTech companies, finance, healthcare, academiaRetail, marketing, consulting, finance

The Graduate Data Science Intern role typically involves working on machine learning models and advanced analytics, requiring a background in data science or related fields. In contrast, Data Analyst Interns focus more on data cleaning, visualization, and reporting. Both roles are entry-level, often in similar industries, but differ in technical depth and project scope.

What are popular job titles related to Graduate Data Science Intern jobs in Portland, OR? For Graduate Data Science Intern jobs in Portland, OR, the most frequently searched job titles are:
What job categories do people searching Graduate Data Science Intern jobs in Portland, OR look for? The top searched job categories for Graduate Data Science Intern jobs in Portland, OR are:
Mathematical Statistician (Data Scientist) - Direct Hire

Mathematical Statistician (Data Scientist) - Direct Hire

US Department of the Treasury

Portland, OR

$74K/yr

Other

Posted 5 days ago


U.S. Department Of The Treasury rating

8.2

Company rating: 8.2 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

235th of 692 rated public administrative organizations


Job description

WHAT IS DATA AND ANALYTICS?
A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions

  • Position(s) are to be filled in the following area(s):
    • DAO- Data and Analytics Office (DAO)-RESEARCH, APPLIED ANALYTICS & STATISTICS (RAAS)
  • Consider each location carefully when applying. If you are selected for a location, that location will become your official post of duty.
REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILSQualifications:Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the cut-off dates as shown in announcement under the 'How to Apply' section.
IOR BASIC REQUIREMENTS GS-1529 Mathematical Statistician (Data Scientist):
You must have a degree that included courses in mathematics and statistics totaling at least 24 semester hours. This course work must have included a minimum of 12 semester hours of mathematics, and 6 semester hours were in statistics. Courses acceptable toward meeting the mathematics course requirement must have included at least four of the following: differential calculus, integral calculus, advanced calculus, theory of equations, vector analysis, advanced algebra, linear algebra, mathematical logic, differential equations, or any other advanced course in mathematics for which one of these was a prerequisite. Courses in mathematical statistics or probability theory with a prerequisite of elementary calculus or more advanced courses will be accepted toward meeting the mathematics requirements, with the provision that the same course cannot be counted toward both the mathematics and the statistics requirement.
OR
Combination of education and experience -- includes at least 24 semester hours of mathematics and statistics, including at least 12 hours in mathematics and 6 hours in statistics, as described above; and Experience that showed evidence of statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying known statistical techniques to data such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance.
AND
GS-1529-11 SPECIALIZED EXPERIENCE: To be eligible for this position at this grade level, you must meet the following requirements. In addition to the basic requirements, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-09 grade level in the Federal service. Examples of specialized experience for this position may include:
  1. Experience using data mining process models (such as CRISP-DM, SEMMA, etc.,) to design and execute data science projects.
  2. Experience preparing and analyzing structured and unstructured datasets to explorations and evaluating data science centric models.
  3. Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration.
  4. Experience coding in various programming languages (such as R, Python, SQL, or JAVA) to conduct various phases of data science projects.
  5. Experience creating and querying different datastores and architectures (such as Sybase, Oracle, and open-source databases) to work with various types of data as part of the data science project.
  6. Experience using tools for data visualization (graphs, tables, charts, etc.,) and end-user business intelligence.
OR
EDUCATION: You may substitute education for specialized experience specialized experience as follows: Three (3) full academic years of progressively higher-level graduate education in Mathematics, statistics, or related fields.
OR
Ph. D. or equivalent doctoral degree Mathematics, statistics, or related field of study from an accredited college or university.
OR
Combination of education and experience: A combination of qualifying graduate education and experience equivalent to the amount required.
GS-1529-12 SPECIALIZED EXPERIENCE: To be eligible for this position at this grade level, you must meet the following requirements. In addition to the basic requirements, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-11 grade level in the Federal service. Examples of specialized experience for this position may include:
  1. Experience applying knowledge of statistical theories, principles, concepts and practices that relate to experimental design, data analysis, sampling, forecasting, quality control, and operations research to understand, model and improve program operations.
  2. Experience using data mining process models (such as CRISP-DM, SEMMA, etc.,) to design and execute data science project.
  3. Experience preparing and analyzing structured and unstructured datasets to explorations and evaluating data science centric models.
  4. Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration.
  5. Experience coding in various programming languages (such as R, Python, SQL, or JAVA) to conduct various phases of data science projects.
  6. Experience creating and querying different datastores and architectures (such as Sybase, Oracle, and open-source databases) to work with various types of data as part of the data science project.
  7. Experience using tools for data visualization (graphs, tables, charts, etc.,) and end-user business intelligence.

GS-1529-13 SPECIALIZED EXPERIENCE: To be eligible for this position at this grade level, you must meet the following requirements. In addition to the basic requirements, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-12 grade level in the Federal service.
Examples of specialized experience for this position may include:
  1. Experience applying project management principles on a data science project.
  2. Experience planning and executing a variety of data science and/or analytics projects.
  3. Experience using data mining process models (such as CRISP-DM, SEMMA, etc.,) to design and execute data science project.
  4. Experience preparing and analyzing structured and unstructured datasets to explorations and evaluating data science centric models.
  5. Experience working with multiple data types and formats as a part of a data science project.
  6. Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration.
  7. Experience coding in various programming languages (such as R, Python, SQL, or JAVA) to conduct various phases of data science projects.
  8. Experience creating and querying different datastores and architectures (such as Sybase, Oracle, and open-source databases) to work with various types of data as part of the data science project.
  9. Experience using tools for data visualization (graphs, tables, charts, etc.,) and end-user business intelligence.
AND
You must also meet the following requirements:
  • MINIMUM AGE REQUIREMENT: Minimum age for federal employment is 18 years old, or at least 16 years old and have:
    • Graduated from high school or been awarded a certificate equivalent to graduating from high school; or
    • Completed a formal vocational training program; or
    • Received a statement from school authorities agreeing with your preference for employment rather than continuing your education

For more information on qualifications please refer to OPM's Qualifications Standards.Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER

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