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7 Entry Level Data Scientist/Analyst/Engineer 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/Analyst/Engineer resume that is clear, focused, and easy to review. You will see how to organize evidence, choose strong sections, and compare professional resume formats.

A strong resume for this role should connect technical learning to practical proof. Useful evidence can include statistics, machine learning, data analysis, computer vision, NLP, time series work, software projects, testing, documentation, and clear technical communication.

The same resume is presented in 7 ATS-friendly resume layouts so you can compare reading order, section balance, photo options, and space for projects, education, skills, credentials, and experience.

What Makes a Great Entry Level Data Scientist/Analyst/Engineer Resume in 2026

Strong resumes are easy to scan, evidence-based, and focused on relevant projects, skills, education, and results.

Use clear sections and accurate keywords such as Python, statistics, machine learning, SAS, Tableau, NLP, time series analysis, Java, REST API, Docker, Jenkins, and software development life cycle.

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 single reading path helps recruiters move from summary through experience, projects, education, and skills without side-to-side scanning.

 
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Why This Format Works

  • Linear reading keeps experience, projects, education, and skills in a clear sequence.
  • Full-width sections give project evidence and achievement bullets room to stay readable.
  • Simple section breaks make credentials and technical strengths easy to locate.

Best For

  • Simple recruiter scanning
  • Project-heavy early career resumes
  • Clear education-to-project progression
  • Longer technical bullet points
  • Straightforward ATS text order

Resume Keywords

  • Computer Science
  • Engineering
  • statistics
  • Science
  • data science
  • machine learning
  • Bachelor's Degree
  • SAS
  • Python
  • computer vision
  • data visualization
  • NLP
  • text mining
  • Tableau
  • time series analysis
See all keywords
 

#2 Plain One Column with Shaded Labels Resume Example

Shaded section labels create quick visual stops while the one-column body keeps every resume section in one simple reading order.

 
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Why This Format Works

  • Shaded labels separate experience, projects, education, skills, and credentials at a glance.
  • One continuous body keeps career and academic evidence easy to follow.
  • Strong section cues help reviewers return quickly to key resume categories.

Best For

  • Fast section-by-section review
  • Resumes with many named sections
  • Readers who prefer strong labels
  • Balanced project and education content
  • Simple content with clearer visual breaks
 

#3 One Column Format with Photo Right Resume Example

A right-side photo sits in the header while the one-column body preserves a direct path through the main resume sections.

 
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Why This Format Works

  • Single-column content keeps experience, projects, education, and skills in order.
  • The header photo stays separate from achievement and credential sections below.
  • Full-width body space supports readable project summaries and technical evidence.

Best For

  • Markets where resume photos are standard
  • Direct one-column reading
  • Project detail needing full width
  • Clear separation between header and content
  • Applications requiring visible personal identification
 

#4 Clean Two Column Sidebar Format Resume Example

A clean sidebar separates supporting details from the main two-column resume body so recruiters can scan core evidence and secondary information efficiently.

 
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Why This Format Works

  • A sidebar can group skills and credentials beside the main experience narrative.
  • The wider column preserves space for projects, achievements, and career evidence.
  • Two-column separation helps reviewers distinguish core history from supporting qualifications.

Best For

  • Dense skill and credential sections
  • Compact two-column organization
  • Main experience with supporting details
  • Quick access to secondary qualifications
  • Resumes balancing projects and technical skills
 
 

#5 Two Column Format Resume Example

Two balanced columns divide resume content into parallel areas, helping recruiters compare primary experience with supporting education, skills, and credentials.

 
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Why This Format Works

  • Parallel columns reduce vertical length while keeping major resume categories distinct.
  • Balanced space supports both experience detail and compact supporting qualification sections.
  • Clear column boundaries help reviewers compare career evidence with education and skills.

Best For

  • Balanced primary and secondary content
  • Compact resumes with several sections
  • Side-by-side category scanning
  • Mixed project, education, and skills evidence
  • Readers comfortable with parallel structure
 

#6 Photo Header Two Column Format Resume Example

A photo header adds identity at the top while the two-column body divides core experience from supporting resume details for faster review.

 
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Why This Format Works

  • Two body columns separate experience and projects from supporting resume categories.
  • The photo remains in the header without replacing space for evidence below.
  • Parallel sections let reviewers scan achievements, education, skills, and credentials efficiently.

Best For

  • Regions where resume photos are expected
  • Two-column resumes with visible identity
  • Compact project and skills organization
  • Separate core and supporting sections
  • Applications with several qualification categories
 

#7 Simple Two Column Format Resume Example

A simple two-column structure keeps the page compact while giving recruiters separate areas for core evidence and supporting resume sections.

 
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Why This Format Works

  • Straightforward columns separate detailed experience from shorter supporting resume sections.
  • Compact organization helps reviewers scan projects, education, skills, and credentials quickly.
  • Simple section treatment keeps achievements readable without adding heavy visual distractions.

Best For

  • Clean two-column resumes
  • Compact technical project content
  • Distinct primary and supporting sections
  • Readers who prefer restrained styling
  • Resumes with many short qualification blocks
 
 

Why Build Your Entry Level Data Scientist/Analyst/Engineer Resume with ResumeInMinutes

ResumeInMinutes helps you turn coursework, projects, research, technical skills, certifications, and activities into a resume that is easy to scan and simple to adjust.

  • ATS-friendly section structure
  • Keyword-focused resume content
  • Project and research organization
  • Technical skills and tools sections
  • Free PDF resume previews
  • Resume customization support

You can use these formats to present data science, analytics, machine learning, software, research, and academic evidence while keeping the resume focused on the exact target role.

 

See Also

 

Frequently Asked Questions

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

They use clear sections and readable headings that support ATS scanning. Your results still depend on accurate keywords, clean content, and the employer's ATS.

Are these 7 different resumes?

No. This guide shows one Entry Level Data Scientist/Analyst/Engineer resume in different layouts and design styles.

What should an Entry Level Data Scientist/Analyst/Engineer resume include?

Include a focused summary, education, relevant experience, projects, technical skills, tools, certifications, and measurable or verifiable project evidence when available.

Which keywords are useful for this resume?

Useful keywords may include Python, statistics, machine learning, SAS, computer vision, NLP, text mining, Tableau, time series analysis, Java, Docker, Jenkins, REST API, and software development life cycle.

Can I download the resume templates as PDFs?

Yes. Each resume style provides a PDF preview so you can compare the format before choosing one for your application.

Which layout works best for project-heavy entry-level resumes?

Choose a layout that gives enough space to projects, education, skills, and technical evidence. One-column formats favor simple reading order, while two-column formats can group secondary details more compactly.

Should I include a photo on a U.S. resume?

For most U.S. applications, a no-photo resume is generally the safer choice. Use a photo only when local or industry practice clearly supports it.

Can I customize these resume templates?

Yes. You can adjust the resume text, section order, design style, and PDF format while keeping the content aligned with the Entry Level Data Scientist/Analyst/Engineer role.

ATS score and resume checks

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ATS score 75%

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: 28 of 48 (58%) · Resume quality: 100%

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

Details
  • ✓Name present: Elijah Nunez
  • ✓Email format: elijah.nunez@example.com
  • ✓Phone format: (417) 555-9038
  • ✓Experience 1: Job title: Data Science and Machine Learning Capstone Student, Capstone Project
  • ✓Experience 1: Company name: Academic Research
  • ✓Experience 1: Employment dates: January 2026 - September 2026
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Computer Vision Research Student, University Research Lab
  • ✓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: Software and Data Engineering Student, Course Project
  • ✓Experience 3: Company name: University Course Program
  • ✓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
Elijah Nunez
(417) 555-9038
elijah.nunez@example.com
linkedin.com/example/elijahnunez
Springfield, MO 65806
SUMMARY
Computer Science student building practical evidence across data science, machine learning, software programming, and computer vision. Academic work applies Python, SAS, statistics, Tableau, NLP, text mining, time series analysis, and data visualization to structured and unstructured datasets. Capstone research included a 60,000 record dataset, reproducible notebooks, model comparison, forecasting, evaluation, and clear presentation of limitations. Software coursework added Java, JavaScript, C++, Spring Boot, microservices, Docker, Jenkins, and REST API development within a documented software development life cycle. Research experience strengthened reproducibility, technical writing, verbal communication, and careful interpretation of results. Current studies support entry level technical work where curiosity, self directed learning, and practical experimentation can contribute to thoughtful analysis and dependable software delivery.
EDUCATION
Bachelor's Degree in Computer Science
Missouri State University
GPA: 4.0
2027
Springfield, MO
Coursework: Statistics, machine learning, computer vision, software development, data visualization
TECHNICAL SKILLS
Programming Languages: Python, Java, JavaScript, C++
Statistical Analysis: Statistics, SAS, descriptive analysis
Machine Learning: Model evaluation, supervised learning, feature preparation
Data Visualization: Tableau, trend visualization, model output views
Natural Language Processing: NLP, text mining, document classification
Time Series Analysis: Forecasting, temporal subsets, held out periods
Computer Vision: Image classification, training splits, validation splits
Java Development: Core Java, Spring Boot, REST API
Service Architecture: Microservices, interfaces, request response design
DevOps Tools: Docker, Jenkins, build automation
Software Development: Software development life cycle, testing, source control
Data Engineering: Data preprocessing, reproducible notebooks, analytical datasets
SKILLS
Statistics
Python
Machine learning
Data science
Data analytics
Computer vision
NLP
Text mining
Tableau
Time series analysis
Data visualization
Java
JavaScript
C++
Technical communication
EXPERIENCE
Data Science and Machine Learning Capstone Student, Capstone Project
Academic Research
January 2026 - September 2026
Springfield, MO
Completed supervised capstone research using a mixed dataset, combining statistical analysis, machine learning, NLP, forecasting, visualization, documentation, and presentation for faculty review.
Defined prediction goals and preprocessing steps for a 60,000 record dataset using Python notebooks.
Compared machine learning models through validation results, error review, and documented selection criteria.
Tested time series assumptions against held out periods and presented forecast uncertainty with trend findings.
Evaluated NLP and text mining experiments, recording representative classification failures for faculty discussion.
Built Tableau views connecting statistical findings with model outputs for a 16 person audience.
Maintained source control, testing artifacts, and project notes consistent with software development life cycle practices.
Computer Vision Research Student, University Research Lab
University Research Lab
September 2025 - December 2025
Springfield, MO
Supported supervised computer vision research through Python prototyping, SAS comparison, statistical review, visualization, reproducibility, and faculty communication.
Built a Python computer vision prototype with documented training and validation splits for image analysis.
Reviewed class balance and misclassified images to identify data quality and model behavior issues.
Compared descriptive results in SAS and Python, confirming core calculations across separate analytical workflows.
Prepared visualizations and a research memo covering methods, evidence, limitations, and next experiments.
Organized scripts and environment notes so another student could reproduce analysis without undocumented settings.
Presented research findings to faculty and answered questions about scope, limitations, and future hypotheses.
Software and Data Engineering Student, Course Project
University Course Program
January 2025 - May 2025
Springfield, MO
Developed supervised software and data engineering coursework spanning Java services, APIs, containers, build automation, testing, and presentation.
Developed a Java application exposing a REST API for a small analytical dataset.
Implemented Spring Boot services with microservice style components and documented interface behavior.
Containerized application components with Docker and configured Jenkins build and test steps.
Added JavaScript results views and completed C++ exercises covering data structures and debugging.
Followed software development life cycle stages from requirements through design, testing, and demonstration.
Delivered setup instructions, API examples, test evidence, and presentation materials for course reviewers.
PROJECTS
Predictive Analytics and Forecasting Study
2026
Created a reproducible Python study combining statistical checks, model evaluation, time series forecasting, and Tableau communication for a mixed academic dataset.
Text Classification and Vision Lab
2025
Explored NLP, text mining, and computer vision through labeled academic data, documented errors, and presented findings with clear evidence and limitations.
LEADERSHIP & AWARDS
Dean's List, Missouri State University, 2025 and 2026
Undergraduate Research Presentation Recognition, 2026
Computer Science Academic Scholarship, 2025
CERTIFICATIONS
IBM SkillsBuild Artificial Intelligence Fundamentals
2026
Coursera Machine Learning Specialization
2026
Docker Foundations Learning Course
2026
PROFESSIONAL AFFILIATIONS
Computer Science Student Association, Peer Coding Session Volunteer, 2025 to 2026
Undergraduate Research Forum, Student Presenter, 2026
Campus Data Challenge, Team Participant, 2025
LANGUAGES
English (Native)
Spanish (Proficient)
ADDITIONAL INFORMATION
Work Status
: Authorized to work in United States. No sponsorship required.
REFERENCES
AVAILABLE ON REQUEST

Matched keywords (28)

  • Computer Science
  • Engineering
  • statistics
  • Science
  • data science
  • machine learning
  • Bachelor's Degree
  • SAS
  • Python
  • computer vision
  • data visualization
  • NLP
  • text mining
  • Tableau
  • time series analysis
  • Java
  • software development life cycle
  • verbal communication
  • Core Java
  • JavaScript
  • C++
  • software programming
  • Spring Boot
  • microservices
  • Docker
  • Jenkins
  • REST API
  • self directed learning

Missing keywords (20)

  • Entry Level Data Scientist
  • Analyst
  • Engineer
  • internship
  • recent graduates
  • mathematics
  • IT industry
  • Master's degree
  • Computer Engineering
  • Electrical Engineering
  • Information Systems
  • Information Technology
  • academic projects
  • project work
  • written communication
  • U.S. citizens
  • STEM extension
  • H1B
  • green card
  • technical curiosity
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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