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7 Entry Level Data Scientist Resume Templates with Example

By Ria · Last Updated On September 25, 2026
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This guide shows you how to create an Entry Level Data Scientist resume that is clear, focused, and easy to review. You will learn how to organize strong evidence and compare professional resume formats.

A strong resume for this role should connect technical skills with practical project evidence. Use concise bullets to show data preparation, model evaluation, exploratory data analysis, visualization, cloud exposure, and clear communication, then support those claims with measurable project details when available.

The same complete resume is presented in 7 ATS-friendly layouts so you can compare reading order, section emphasis, and visual structure. Each example includes format guidance to help you choose a layout that keeps experience, education, projects, certifications, and technical skills easy to find.

What Makes a Great Entry Level Data Scientist Resume in 2026

A strong resume is focused, evidence-based, and easy to scan. It should make project scope, analytical thinking, model evaluation, and communication clear without overstating results.

Match the role with accurate keywords such as Python, Machine Learning, EDA, Pandas, Scikit-learn, TensorFlow, Tableau, Microsoft Azure, Amazon Web Services, NLP, and Deep Learning, then support them with project or research evidence.

The examples shown below are intentionally detailed to demonstrate a range of experiences, skills, accomplishments and relevant keywords. We prefer detailed resumes as they provide more context to the hiring managers and also help the ATS systems identify relevant qualifications and score your resume better for a match. But the length of a resume is an individual choice. If you want a one page resume, just keep the most relevant experience and information for the job. Note: Resume examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organizations do not imply affiliation or endorsement.

#1 Simple One Column Format Resume Example

A simple one-column flow gives recruiters a direct reading path through each resume section.

 
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Why This Format Works

  • Linear reading order keeps experience and achievements easy to follow.
  • Full-width sections give skills and education clear, separate space.
  • Simple section breaks help credentials stay visible during a quick review.

Best For

  • Straightforward recruiter scanning
  • Detailed project evidence
  • Clear education and credentials
  • Applications with many technical skills
  • Readers who prefer a linear flow

Resume Keywords

  • Python
  • machine learning
  • artificial intelligence
  • EDA
  • NumPy
  • Pandas
  • Scikit learn
  • Seaborn
  • Plotly
  • TensorFlow
  • Tableau
  • Excel
  • Microsoft Azure
  • Amazon Web Services
  • Linear Regression
See all keywords
 

#2 Plain One Column with Shaded Labels Resume Example

Shaded section labels make the one-column resume easier for recruiters to scan by topic.

 
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Why This Format Works

  • Shaded labels separate experience, skills, education, and credentials without changing reading order.
  • One-column structure keeps achievements connected to the experience sections they support.
  • Visible section markers help reviewers return quickly to major content groups.

Best For

  • Fast section-by-section review
  • Longer experience descriptions
  • Clearly separated credentials
  • Project-heavy resumes
  • Readers who value strong section markers
 

#3 One Column Format with Photo Right Resume Example

A right-side photo adds personal identification while the one-column body keeps recruiter scanning direct.

 
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Why This Format Works

  • One-column body keeps experience and achievements in a clear sequence.
  • Separate sections give skills, education, and credentials predictable places to review.
  • Right-side photo stays in the header without dividing the resume body.

Best For

  • Markets where resume photos are customary
  • Linear career and project review
  • Strong separation of skills and education
  • Resumes with substantial written evidence
  • Readers who want an identifiable header
 

#4 Clean Two Column Sidebar Format Resume Example

A clean two-column sidebar helps recruiters separate supporting details from the main resume story.

 
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Why This Format Works

  • Two columns separate supporting skills and credentials from longer experience content.
  • Main-column space keeps achievements readable while secondary details remain easy to locate.
  • Sidebar organization lets education and skills stay visible without interrupting career flow.

Best For

  • Compact technical skills groups
  • Experience needing more narrative space
  • Easy access to supporting credentials
  • Balanced main and secondary content
  • Reviewers comparing several qualification categories
 
 

#5 Two Column Format Resume Example

A balanced two-column structure lets recruiters compare core experience with supporting resume sections quickly.

 
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Why This Format Works

  • Parallel columns create clear zones for experience, skills, education, and credentials.
  • Balanced space helps dense resume content remain organized across several section types.
  • Separated content areas support quick comparison between achievements and supporting qualifications.

Best For

  • Resumes with several content categories
  • Balanced experience and skills coverage
  • Compact education and certification details
  • Readers comparing qualifications side by side
  • Applications needing efficient use of space
 

#6 Photo Header Two Column Format Resume Example

A photo header identifies the applicant while two columns help recruiters scan the resume by section.

 
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Why This Format Works

  • Two-column body divides experience from supporting skills, education, and credentials.
  • Distinct content areas keep achievements readable even with a more visual header.
  • Header photo adds identification while body organization carries the qualification details.

Best For

  • Countries where resume photos are expected
  • Two-column qualification review
  • Resumes balancing experience with credentials
  • Applications needing a visible personal header
  • Readers scanning several section types
 

#7 Simple Two Column Format Resume Example

A simple two-column layout gives recruiters a compact view of primary and supporting resume content.

 
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Why This Format Works

  • Simple columns keep experience and achievements distinct from shorter supporting sections.
  • Side-by-side organization gives skills and credentials clear space without crowding descriptions.
  • Consistent section blocks help reviewers move between education and career evidence efficiently.

Best For

  • Compact resumes with varied sections
  • Clear split between major content groups
  • Technical skills needing dedicated space
  • Career evidence with supporting credentials
  • Readers who prefer concise visual organization
 
 

Why Build Your Entry Level Data Scientist Resume with ResumeInMinutes

ResumeInMinutes can help you turn academic projects, research work, technical skills, education, certifications, and measurable results into a resume that is simple to review and easier to tailor.

  • ATS-friendly section structure
  • Role-focused keyword guidance
  • Project and experience organization
  • Technical skills grouping
  • Free PDF resume previews
  • Resume customization support

You can adjust the resume for a specific Entry Level Data Scientist posting while keeping the evidence accurate. Focus your edits on the skills, projects, tools, and results that match the role instead of adding unsupported qualifications.

 

See Also

 

Frequently Asked Questions

Are these Entry Level Data Scientist resume templates ATS-friendly?

They use clear section structure and readable headings that support ATS scanning. Your results still depend on accurate content, relevant keywords, and a clean final file.

Are these different candidate resumes?

No. The examples show the same Entry Level Data Scientist resume in different visual formats so you can compare organization without changing the underlying evidence.

What should an Entry Level Data Scientist resume include?

Include a focused summary, education, relevant project or research experience, technical skills, certifications, and measurable evidence. Show how you prepared data, evaluated models, visualized findings, and communicated results.

Can I download the resume templates as PDFs?

Yes. Each resume style includes a free PDF preview so you can review the format before choosing the version you want to customize.

Which resume style works well for an Entry Level Data Scientist application?

Choose a format that keeps experience, projects, education, skills, and certifications easy to scan. A clean one-column or balanced two-column layout can work well when the reading order stays clear.

Can a student or early-career candidate build a strong data science resume?

Yes. Use academic projects, research, presentations, certifications, and technical work as evidence. Add numbers when they are accurate, such as dataset size, audience size, or project scope.

Should I include a photo on my resume?

Include a photo only when it is customary or expected in the country or market where you apply. For most U.S. applications, a no-photo resume is the safer choice.

Can I customize these resume templates?

Yes. You can adjust the resume text, section order, layout, and design for a specific Entry Level Data Scientist role while keeping every claim accurate and supported.

ATS score and resume checks

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ATS score 87%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your resume is used. This is not a match to a specific vacancy.
  • Text-based resume: 20%. The text resume assessed here receives 100 for this part.
  • Resume quality: 20%. We start at 100 and subtract points for issues found by our resume checks, down to a minimum of zero.
  • Keyword coverage = 100 × (confirmed matches + 0.8 × possible matches) ÷ total keywords, rounded and capped at 100. Possible matches still need checking. Saved generation audits use their saved keyword percentage.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based resume 20% · Resume quality 20%

Live keyword match: 40 of 51 (78%) · Resume quality: 100%

Resume quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Brianna Kramer
  • ✓Email format: brianna.kramer@example.com
  • ✓Phone format: (608) 555-1842
  • ✓Experience 1: Job title: Data Scientist Capstone Student, Analytics Workflow
  • ✓Experience 1: Company name: Capstone Project
  • ✓Experience 1: Employment dates: January 2026 - September 2026
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Machine Learning Research Student, Text and Image Modeling
  • ✓Experience 2: Company name: University Research Lab
  • ✓Experience 2: Employment dates: September 2025 - December 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Applied Data Analytics Student, Public Dataset Study
  • ✓Experience 3: Company name: Course Project
  • ✓Experience 3: Employment dates: January 2025 - May 2025
  • ✓Experience 3: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based Resume 100% · Weight 20%

Parsed text
Brianna Kramer
(608) 555-1842
brianna.kramer@example.com
linkedin.com/example/briannakramer
Madison, WI 53703
SUMMARY
Data science student building practical solutions through machine learning, artificial intelligence, exploratory data analysis, and clear visualization. Academic projects have covered structured and unstructured data, automated collection, preprocessing, regression, classification, clustering, natural language processing, and image classification. Python work with NumPy, Pandas, Scikit learn, Seaborn, Plotly, TensorFlow, Tableau, and Excel has supported repeatable analysis and thoughtful model comparison. Experience with Microsoft Azure and Amazon Web Services adds a useful cloud foundation. Careful documentation, data quality checks, validation review, and plain language presentations have helped mixed audiences follow technical decisions. Collaborative research and capstone work strengthened analytical reasoning, written communication, reading comprehension, and attention to detail. Ready for an entry level data science setting where curiosity, steady learning, and practical analysis can support sound decisions.
EDUCATION
Bachelor's Degree in Computer Science
University of Wisconsin Madison
GPA: 4.0
2027
Madison, WI
Coursework: Machine Learning, Artificial Intelligence, Data Mining, Statistical Analysis, Database Systems
TECHNICAL SKILLS
Programming Languages: Python, Python scripting, Jupyter Notebooks
Data Analysis Libraries: NumPy, Pandas, data preprocessing
Machine Learning: Scikit learn, Linear Regression, Logistic Regression
Classification Models: Decision Tree, Random Forest Classifier, Naive Bayes
Advanced Models: Support Vector Machine, clustering, model evaluation
Visualization Tools: Seaborn, Plotly, Tableau
Business Spreadsheet Tools: Microsoft Excel, data visualization, chart design
Deep Learning: TensorFlow, image classification, neural networks
Natural Language Processing: NLP, text classification, unstructured data
Cloud Platforms: Microsoft Azure, Amazon Web Services, cloud storage
Data Engineering: automated collection, data lineage, schema checks
Analytics Methods: EDA, exploratory analysis, error analysis
SKILLS
Python
Machine Learning
Artificial Intelligence
Exploratory Data Analysis
Data Preprocessing
Data Visualization
NLP
Deep Learning
Model Evaluation
Statistical Analysis
Problem Solving
Written Communication
Reading Comprehension
Cloud Computing
Data Collection
EXPERIENCE
Data Scientist Capstone Student, Analytics Workflow
Capstone Project
January 2026 - September 2026
Madison, WI
Led a semester scale analytics workflow for a mixed format dataset, shaping data quality checks, reproducible modeling, validation review, visualization, and final communication for a capstone audience.
Defined prediction goals for a 120,000 row dataset and documented quality checks, preprocessing, evaluation criteria
Built reproducible Python pipelines with Pandas and NumPy for missing values, encoding, and standardization
Compared regression, classification, and support vector models, recording validation results and practical limitations
Used Seaborn and Plotly for EDA, then translated distributions and relationships into Tableau views
Tested clustering methods and assessed whether segments added useful context beyond supervised modeling
Presented model tradeoffs and error analysis to 14 peers with organized notebooks and data dictionaries
Machine Learning Research Student, Text and Image Modeling
University Research Lab
September 2025 - December 2025
Madison, WI
Supported faculty supervised research across text classification and image modeling, using documented experiments, cloud resources, evaluation review, and concise communication of findings.
Investigated text classification across a 9,500 document corpus with repeatable preprocessing and baseline models
Prepared unstructured text in Python and Pandas while preserving raw data lineage and feature generation notes
Reviewed precision, recall, confusion matrices, and representative errors across Scikit learn classifiers
Prototyped TensorFlow image classification and documented training settings, validation behavior, and misclassified examples
Used Microsoft Azure resources for controlled experiments and reproduced a smaller workflow on Amazon Web Services
Produced a Plotly report connecting model results with research questions and measured conclusions
Applied Data Analytics Student, Public Dataset Study
Course Project
January 2025 - May 2025
Madison, WI
Completed an end to end public dataset study, combining collection scripting, data checks, exploratory analysis, baseline modeling, visualization, and presentation for a course deliverable.
Created Python collection scripts for a 35,000 record dataset, then assessed source quality and structure
Built checks for duplicates, missing fields, unexpected values, and schema consistency before analysis
Established regression and classification baselines with Scikit learn and compared assumptions before method selection
Explored clustering stability and practical interpretation instead of treating algorithm output as a final conclusion
Developed Tableau and Excel visuals for nontechnical readers alongside Seaborn figures for analytical review
Delivered a documented repository and presentation that supported reproducibility, clear writing, and careful reasoning
PROJECTS
Mixed Format Predictive Analytics
2026
Built a documented predictive workflow for a large mixed format dataset, combining preprocessing, model comparison, clustering, EDA, Tableau storytelling, and peer review.
Cloud Based Text and Image Study
2025
Connected NLP and TensorFlow image classification experiments with Azure and AWS practice, giving research questions a clear and measured analytical response.
LEADERSHIP & AWARDS
Dean's List, 2025 and 2026
Undergraduate Data Analytics Capstone Recognition, 2026
Academic Excellence Scholarship, 2025
CERTIFICATIONS
Google Advanced Data Analytics Professional Certificate
2026
Microsoft Learn Azure Fundamentals Learning Path
2026
AWS Academy Cloud Foundations
2025
PROFESSIONAL AFFILIATIONS
Data Science Student Association, Peer Project Reviewer, 2025 - 2026
Undergraduate Research Forum, Student Presenter, 2026
Campus Analytics Challenge, Team Participant, 2025
LANGUAGES
English (Native)
Spanish (Intermediate)
ADDITIONAL INFORMATION
Work Status
: Authorized to work in United States. No sponsorship required.
REFERENCES
AVAILABLE ON REQUEST

Matched keywords (40)

  • Python
  • machine learning
  • artificial intelligence
  • EDA
  • NumPy
  • Pandas
  • Scikit learn
  • Seaborn
  • Plotly
  • TensorFlow
  • Tableau
  • Excel
  • Microsoft Azure
  • Amazon Web Services
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest Classifier
  • Naive Bayes
  • Support Vector Machine
  • clustering
  • NLP
  • Deep Learning
  • data preprocessing
  • data visualization
  • unstructured data
  • automated collection
  • exploratory data analysis
  • model evaluation
  • image classification
  • Bachelor's Degree
  • Computer Science
  • Python scripting
  • analytical reasoning
  • Problem Solving
  • attention to detail
  • reading comprehension
  • Cloud Platforms
  • data quality
  • Data Collection

Missing keywords (11)

  • Entry Level Data Scientist
  • structured data
  • Computer Engineering
  • Information Systems
  • Information Technology
  • Electrical Engineering
  • Mechanical Engineering
  • English communication
  • data sources
  • data preparation
  • data modeling
Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

Resume and cover-letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organization.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that resume or cover-letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover-Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on resume formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover-letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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