Data Science Student Resume Example

A data science student resume is judged on projects, and projects are judged on what question you answered, with which data, and what the model or the chart changed. The stack (Python, R, SQL) goes in a skills line; the proof goes in bullets with a dataset size and a measured outcome.

The Sample resume below is a junior in Seattle who works as a research assistant in a university lab, interned at a small analytics consultancy and placed in a public data competition. Each entry names the tools, the size of the data and a result a reviewer can picture: a pipeline that replaced manual cleaning, a model whose error dropped, a dashboard a client kept.

Data science internships are competitive and the screen is usually a GitHub link and a take-home. Put the link in the contact block and make sure the top two repositories have a readme that states the question, the data and the finding. For the roles this leads to, read the data scientist example and the data analyst example; the computer science example covers the engineering-heavy end of the same search. The rules every student page shares are on the business student example.

Updated . The person and employers in this Sample resume are fictional.

Data Science Student sample resume

Arjun Raghunathan

Data Science Student, Class of 2027 | Research Assistant | Python, R, SQL | github.com/example

Seattle, WAarjun.raghunathan@example.com(206) 555-0125

Summary

Junior data science major at Puget Sound Technical University, graduating June 2027 with a 3.7 GPA and a statistics minor. Work ten hours a week as a research assistant cleaning and modeling ecological sensor data, completed a summer analytics internship for small-business clients, and placed in the top 5 percent of a public data competition on a demand forecasting problem. Looking for a summer 2026 data science or machine learning internship.

Experience

Undergraduate Research Assistant, Computational Ecology Lab, Puget Sound Technical University

– Present

Seattle, WA

  • Built a Python pipeline (pandas, scikit-learn) that cleans and merges readings from 120 stream temperature sensors, about 9 million rows a year, replacing a manual process that took a graduate student two days each month.
  • Fit gradient-boosted models to predict missing sensor readings; mean absolute error fell from 0.9 to 0.4 degrees against the lab's previous interpolation method.
  • Wrote the lab's data dictionary and a reproducible analysis notebook used by two graduate students for their thesis chapters.
  • Co-authored a poster presented at the university's undergraduate research symposium in May 2025.
Data Analytics Intern, Rainier Analytics Group

–

Seattle, WA

  • Wrote SQL against clients' PostgreSQL and Snowflake warehouses to build weekly sales and inventory reports for three retail clients, replacing exports the consultants had been reshaping in Excel.
  • Built a Tableau dashboard for a 22-store regional grocer that flagged slow-moving SKUs; the client used it to cut a product line and still checks it weekly.
  • Ran an A/B test readout for an email campaign, including the power calculation the client had skipped, and recommended extending the test two more weeks before deciding.
Team Lead, Public Data Competition (demand forecasting), Independent project, team of three

–

Seattle, WA

  • Led a three-student team forecasting daily store-item demand for a public competition dataset of 3 million rows, finishing in the top 5 percent of about 1,900 entries.
  • Engineered lag and holiday features and ensembled LightGBM with a seasonal baseline, which beat our best single model by 6 percent on the leaderboard metric.
  • Published the code and a write-up on GitHub; the write-up is the first repository recruiters are pointed to.

Education

Bachelor of Science in Data Science, minor in Statistics (expected June 2027)

Puget Sound Technical University, Seattle, WA

GPA 3.7. Courses: Machine Learning, Statistical Inference, Regression and Time Series, Databases and SQL, Data Structures, Linear Algebra, Probability, Data Visualization, Experimental Design. Teaching assistant for Introduction to Data Science, spring 2026.

Skills

  • Python (pandas, NumPy, scikit-learn, LightGBM)
  • R (tidyverse, ggplot2)
  • SQL (PostgreSQL, Snowflake)
  • Tableau and Plotly
  • Jupyter and reproducible notebooks
  • Git and GitHub
  • Statistical inference and A/B testing
  • Time series forecasting
  • Feature engineering
  • Data cleaning at scale
  • Linux command line
  • Technical writing

Certifications

  • Tableau Desktop Specialist, Tableau,

Start yours from this example

Opens this sample in the editor, in one of the templates below. Swap in your details, then tailor it to each posting in about two minutes.

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Templates for the data science student resume

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How the resume changes with experience

The same data science student resume, written at three levels: a summary and three bullets for each.

Entry level

Zero to two years, or a first role in the title

Second-year data science student who has completed Introduction to Data Science, Probability and Databases, with two personal projects on GitHub and a part-time tutoring job, applying for a first research assistant position or a data internship.

  • Analyzed four years of a city's open transit data in Python to show which bus routes lost riders after a 2023 schedule change, with charts and a write-up on GitHub.
  • Tutor eight students a week in introductory statistics through the campus learning center, which has made me far better at explaining a p-value.
  • Picked a messy public dataset over a clean tutorial one for my second project because cleaning is what the job is.

Mid level

Three to seven years, owns a process or a workload

Junior data science student with a research assistant role, an analytics internship and a top 5 percent finish in a public forecasting competition, applying for summer 2026 data science and machine learning internships.

  • Built a pipeline for 9 million sensor rows a year that replaced two days of manual cleaning a month in a university lab.
  • A gradient-boosted imputation model cut mean absolute error from 0.9 to 0.4 degrees against the lab's prior method.
  • Recommended a client extend an underpowered A/B test rather than call a winner, after running the power calculation they had skipped.

Senior

Eight years or more, leads people, budgets or programs

Graduating data science senior with two internships, a lab publication in review and a senior capstone deployed for a campus department, seeking a full-time data scientist or machine learning engineer role starting July 2027.

  • Second internship on a logistics company's data team shipped a delivery-time model into production behind an API, with monitoring for drift.
  • Second author on a manuscript from the ecology lab, with the sensor imputation method as the methods contribution.
  • Chose a smaller company's offer over a larger brand's analyst role because the smaller team would have me deploying models in the first year.

Skills to put on the data science student resume

Hard skills

Python with the standard data stack
pandas and scikit-learn are assumed; naming them with a project size shows you have used them beyond a course notebook.
SQL on a real warehouse
Most internship take-homes start with a SQL question, and writing queries against PostgreSQL or Snowflake for a client is the strongest answer.
Statistical inference
A/B tests, power calculations and confidence intervals separate data science candidates from people who can only call fit and predict.
A modeling project with a measured result
An error that fell from 0.9 to 0.4 is a line a hiring manager can interrogate in an interview, which is what they want.
Visualization and dashboards
Tableau, Plotly or ggplot2 work shows you can hand a result to a non-technical person, which is half the internship.
Git and a readable GitHub
Recruiters click the link; two repositories with clear readmes do more than ten without.
R alongside Python
Research labs and many analytics teams still work in R, and listing both widens the set of internships you fit.

Soft skills

Asking what the question is
Students who start with the problem rather than the model produce work a client can use, and interviewers probe for this.
Explaining a result plainly
Tutoring, TAing or presenting a poster all prove you can translate a model for someone who will never read the code.
Skepticism about your own numbers
A bullet about extending an underpowered test or checking a leak tells a team you will not ship a bad finding.
Documentation habits
A data dictionary or a reproducible notebook is the kind of unglamorous work a lab or a team remembers you for.

Mistakes that cost data science student interviews

  1. A skills list longer than the projects

    Fifteen libraries and three projects reads backwards. Trim the skills to what you have used in a project on the page, and give each project the space to say the question, the data size, the method and the result.

  2. Projects from tutorials presented as your own

    Reviewers recognize the common datasets and the standard notebook. If a project started from a tutorial, say what you added; better, pick a public dataset no course uses and ask a question nobody has answered on it.

  3. Accuracy without a baseline

    A model with 94 percent accuracy means nothing without the class balance or the method it beat. Report the metric that fits the problem and the baseline it improved on, as the Sample does with mean absolute error against the lab's prior interpolation.

  4. No link, or a link to an empty GitHub

    Put the GitHub link in the contact line and make sure the pinned repositories have a readme with the finding at the top. A recruiter who clicks and finds a fork of a course repo stops reading.

  5. Dropping the research assistant job because it is not industry

    Lab work with real messy data is the best experience most students can get. Write it as a job with the pipeline you built, the rows you handled and what it replaced, and mention the poster or manuscript.

  6. Calling yourself a machine learning engineer

    The headline should state what you are: a data science student with specific work. Overreaching titles trigger harder interview questions than the resume can support.

Questions about the data science student resume

What projects should a data science student put on a resume?

Two or three, each with the question asked, the dataset and its size, the methods, the result against a baseline and a link. A competition entry, a research lab project and a self-chosen analysis of a public dataset make a strong set. Class assignments count only if you extended them beyond the rubric.

Do I need a GitHub for a data science internship?

Yes. Many screens go straight from the resume to the link. Keep two to four repositories pinned, each with a readme that states the finding in the first paragraph, a clear notebook or script and the data source. Archive the rest.

Should I list every Python library I have used?

No. List the ones that appear in your projects, grouped under the language, and let the bullets prove them. Recruiters search for a few terms (Python, SQL, scikit-learn, Tableau, R) and anything beyond that is read as padding.

How do I write a data competition on a resume?

As a project entry with your role, team size, the problem type, the data size, the method and your placing as a percentile or a rank out of the total entries. Say a public data competition rather than relying on the platform name, and link the code.

Is a research assistant job better than an industry internship for a data science student?

They prove different things. The lab shows you can handle messy data and work without a spec; the internship shows you can deliver to a deadline for a client. Having one of each by junior year, as the Sample does, is the strongest position. If you can only get one before junior year, take the lab.

Should a data science student resume be one page?

One page for internships and most entry roles. Projects earn their lines with results; coursework stays in the education detail as a single line of titles. If you have a publication, cite it in one line rather than adding a section.

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