Data Scientist Resume’s & Guide

Last Updated on November 14, 2022

Data Scientist

Data Scientist Resume Samples

(Free sample downloads are at the bottom of this page)

Data Scientist Writing Guide

Resume Sections

1. Contact Information: 

  • Your first name, last name
  • Contact number
  • Email address
  • LinkedIn or alternative links

Keep it simple and up to date, perhaps directing prospective employers to your LinkedIn Profile URL as an alternative contact measure. 

2. Career Summary: 

Without using personal pronouns or too many adjectives, you should create a short and concise career summary. Place this section at the top of your resume page and use different fonts to highlight it. Emphasize your technical skills, industry expertise, and academic background in a paragraph of 3-5 lines.

3. Qualifications Summary: 

Within this industry, you cannot skimp on qualifications. Typical degrees within this field are Ph.D.’s with data analytics or data science as both majors and minors. Only include up-to-date information about your completed qualifications and the ones you are in the process of completing and ensure that you indicate the institution, qualification name, and dates. 

4. Relevant Data Scientist Experience:

Do not simply show all your experience, but only the most pertinent experience according to the job posting. Begin with your most recent positions, and be sure to add 3–5 bullet points with measurable accomplishments.

 Indicate your employment history by providing details regarding the last ten years of experience up until your current position in reverse chronological order (front to back). Using bullet points, list your most important responsibilities.

5. Other Employment Experience: 

If you are a Data Scientist that is just starting out, it can help to focus on your other experiences and technical skill sets that can be replicated in the Data Scientist role, such as having a strong background in programming.  This section should also include work history or projects outside of formal data analysis, especially if you apply for your first Data Scientist. job 

6. Skills Summary/Key Skills

Data Scientists are central in programs like Python, C, R, SQL, and Excel. Technical competencies such as scrubbing, algorithms, data manipulation, and correlations will appear in most job descriptions. Therefore, it is crucial to ensure that your technical competencies are highlighted in the key skills section and integrated into all areas of your resume

7. Education/Licenses/Certifications/Relevant Coursework/Training: 

You can either add a special section for licenses, certifications, and courses or simply include them within your education section. Include conferences, workshops, and seminars you have attended as they are vital to a Data Scientist’s continuous professional development, which shows potential employers you are looking to improve. 

What to Highlight in a Data Scientist Resume

Data Scientists are large data wranglers, constantly gathering and analyzing large sets of both structured and unstructured data. Data Scientist roles combine computer science, statistics, and mathematics. It is one thing to know that you are the perfect candidate for the job. However, convincing potential employers of this is a skill all on its own.

No matter how extensive your work experience is as a Data Scientist, potential employers and recruiters look for particular skills and experience in your resume to ascertain whether your application should be shortlisted.

Therefore, you need to prove to hire managers that you have skills in both technology and social science to find trends and manage data. Focus should be on your industry knowledge and contextual understanding. Provide a few examples of these.

A Data Scientist’s work typically entails ordering messy, unstructured data from sources such as smart devices, social media feeds, and emails that do not fit neatly into a database. Ensure that you clarify and elaborate on the specific type of specialty, or specialties of Data Science you can provide. Below are a few examples: 

  • Machine Learning Engineer: Found at the intersection between software engineering and data science, Machine Learning Engineers can provide an extensive range of software tools and skills and are adept in delivering practicable software solutions.
  • Machine Learning Scientist: Machine Learning Scientists predominantly focus on researching and implementing new approaches and investigating new algorithms. The outputs of a Machine Learning Scientist include reports and whitepapers.
  • Statistician: Statisticians work with both theoretical and applied statistics in a business-orientated way. Using mathematical techniques, Statisticians analyze, interpret, and report statistical information and provide business-relevant conclusions based on the data.
  • Business Intelligence Developer: Using Business Intelligent (BI) tools for creating custom applications for BI analytics, Business Intelligence Developers work on strategies that help business users maximize their decision-making processes.
  • Quality Analyst: Often associated with statistical process control in the manufacturing industry. Mass production assembly lines have large data sets that need to be analyzed to ensure quality control and meet minimum performance standards. 

Once you have outlined which area of Data Science you are involved in, your next step is to focus on the purpose of your role:

  • Machine learning concepts and techniques such as neural networks, supervised machine learning, reinforcement learning, decision trees, adversarial learning, and logistic regression. These skills allow Data Scientists to solve business or organizational problems.
  • Working with unstructured data, which is any data that does not fit neatly into database tables. Tracking structured data, like customer purchase information, is fairly simple. Factoring in things like customer reviews, product photos, social media posts, and other unstructured data can be far more difficult. Data Scientists must be comfortable manipulating this kind of information.
  • Data visualization entails translating data information into a format that can be easily understood by others who cannot read data information. Within data science, a picture is worth a million number sets. Therefore, data scientists need experience working with data visualization tools like Gplot, Matplotlib, and Tableau.

Resume Hack: Include a “tools and tech” section presenting your software application experience to produce reports, model data sets, information categorization, and data visualization.

Microsoft ExcelMinitabQlikView
Google AnalyticsPythonSplunk
Apache SparkRTLR Shiny
Pivot TablesHyper ResearchMAXQD
Google Tag ManagerJavaScript FrameworksC++

Data Scientist Career Summaries & Objectives

There are sections of a Data Scientists' resume that are generic to all job postings and can be copy-pasted from resume to resume. However, your career summary/objective section needs to be original and should mirror what the job description is looking for. You may think rewriting this section for every resume is a waste of time, but it shows that you are original and is likely to make you stand out from other applicants.

Within this section, you can use your experience to capture the reader’s attention. The experience you include in this section allows potential employers to gauge whether you would be a good fit within their company. A resume objective sells your skills and passion. 

Resume Hack: Identify keywords from the job description and integrate them into your career summary. For example, if the job requires advanced coding skills, use the term ” coding, ” not programming. They do in fact mean the same thing, but the Applicant Tracking System (ATS) Bot does not know that and is programmed to look for specific terms and phrases. This technique can significantly increase your chances of landing an interview.


Summary Example 1

“Microsoft and Google certified Data Scientist with eight years of experience. Looking to increase data efficiency for Contranix Capital Inc. Achievements include developing data regression models to predict company stock prices with 30% higher accuracy than the historical average. Achieved 25% improvement in investment returns across all clients. Extremely skilled in machine learning, data visualization, and creative thinking.”

Summary Example 2

“Highly analytical Data Scientist with three years’ experience seeking a new position. Skilled at working with statistics and machine learning with effective critical thinking and data visualization skills.”

Summary Example 3

“Junior Data Scientist with 4+ years of experience in project and freelance jobs. Beat 250+ statistics professors and data professionals in an NCAA pool, by developing models that best fit the problem. Skilled in statistics, machine learning, problem-solving, and programming.”

Employment History, Job Descriptions, and Responsibilities

When it comes to your employment history section, it can be very beneficial to implement a reverse-chronological layout. This allows the reader to make sense of your past employment easily, and, in our opinion, it is the best data science resume format. We recommend it because it puts your most recent work achievements first. See below:


Senior Data Scientist at Citibank

February 2018 – December 2022

  • Fulfilled all data science responsibilities for a high-end capital management firm.
  • Developed and presented models for potential holdings to fund managers. Achieved 25% better returns vs. previous performance.
  • Developed and implemented a machine learning tools that computed adjusted P/E values.
  • Predicted stock price 27% better than traditional figures.
Data Scientist at HSBC

February 2015– January 2018

  • Worked as a Data Scientist in a capital management firm.
  • Developed and presented models for potential holdings.
  • Implemented machine learning tools to compute adjusted PE values. 
  • Fulfilled all data science duties for a high-end capital management firm.

Today, Data Scientists are found in almost every industry with considerable variation in their roles, purposes, and KPI’s. However, a potential employer expects to see proven foundational duties and competencies when reading an applicant’s resume. This depends on the applicant’s educational level and career stage.

Job Duties Samples for Data Scientist

A Data Scientist at the entry-career stage (0-2 years’ experience) may:

  • Be responsible for implementing data mining and statistical machine learning solutions to combat various business problems such as sales lead scoring, demand forecasting, supply chain optimization, and targeted marketing.
  • Develop a customer segmentation algorithm in R that scores sales leads for jets and increases market share.
  • Apply logistic regression models to jet maintenance records that predict jet sales based on aircraft usage and maintenance type.
  • Implement demand forecasting models that improve forecast accuracy and significantly reduced supply chain inefficiencies.

A Data Scientist at mid-career stage (2-4 years’ experience) may:

  • Provide technical leadership for a team that designs and develops analysis systems to extract meaning from large scale data.
  • Mentor high-end organizations on large scale data and analytics using advanced statistical and machine learning models.
  • Architect and implement analytics and visualization components for device data analysis platforms to predict potential hardware problems.
  • Optimize factors for sales conversions and design algorithms for deal recommendations for a large daily deal’s website.
  • Develop audience extension models that rely on decision trees, logistic regression, random forest, and other categorical data.

A Data Scientist at experienced/advanced stage (4-6 years’ experience) may:

  • Design and implement molecular dynamic simulations to identify protein-DNA interactions and macromolecular structures.
  • Develop pipelines to analyze large simulation datasets combining my own Python, Shell, and TCL scripts with recognized molecular modeling tools.
  • Interpret complex simulation data using statistical processes and methods.
  • Implement bioinformatic algorithms for NGS data analysis.
  • Work in a cross-functional team to create a unique multi-scale simulation technique and implement the methodology that explains complex biological phenomena, including viral DNA ejection and transcriptional.

Highlight Your Accomplishments

Data Scientists add value by providing important perspectives into business operations, processes, or trends in a format that is easy for internal and external stakeholders to make sense of so that necessary informed decisions can be made. This is a vitally important section of your resumes as it shows potential employers the skills and experience you can add to their company. 

A few tips regarding accomplishments:

1) Communicate your accomplishments in a concise and ordered manner.

2) Make use of bullet points and bold the most important part of each statement.

3) Make use of industry-specific terminologies to discuss your achievements.

4) Quantify your accomplishments by including numerical values (percentages, dollar amounts, volumes), time frames, and frequencies. 

Below are examples of BLAND statements which is not the aim:

  • Collaborate with team to optimize CRM database for a high-volume real estate firm.
  • Decrease wasted email and phone time. 
  • Use matplotlib to create real-time ROI graphs that ensure the team's focus is on high-profit business, increasing annual profit. 
  • Applied data mining to shipping consolidation problems, which showcased potential savings for single-day shipping consolidation.

Now, examples of statements that you should include!

  • Collaborate with team members to optimize CRM database for a high-volume real estate firm, which increases efficiency by 42% in 15 months
  • Decreased wasted phone and email time by 58%.
  • Applied matplotlib to compile real-time ROI graphs that helped the teams focus on high-profit business. This led to a 24% increase in annual profit.
  • Applied data mining to shipping consolidation problem, which demonstrated potential savings of $3.4 million over 2015 for single-day shipping consolidation.

Data Scientist Education Section Example

Academic qualifications are more important than you may imagine, and your education section forms a vital part of your Data Scientist resume. In short, simply list your qualification, the institution, and the date of completion for each degree, diploma, or accreditation obtained. Do not forget to mention any online courses, training programs, or industry workshops you have attended, and make sure to provide details regarding course curriculums of program topics.

Here are some examples of a Data Scientist‘s education section:

2018-2020 – Master’s Degree in Machine Learning, Southern Methodist University, Winbrooks, SC

Course Topics:

  • Programming
  • Machine Learning techniques
  • Data Visualization and Reporting
  • Risk Analysis
  • Statistical analysis and Math
  • Effective Communication
  • Software Engineering Skills
  • Data Mining, Cleaning, and Munging

2015-2017 – BS in Statistics, Syracuse University

2014 – Microsoft Professional Program for Data Science, Microsoft Learning Academy, Online. Completed all ten required courses in Data Science (125 hours).

Mastered eight crucial data science skills.

2013 – SAS Certified Predictive Modeler using SAS Enterprise Miner 14Institute for Operations Research and the Management Sciences (INFORMS), Online.

Writing a Data Scientist Resume Skills Section

Technical competencies are not the only things that matter. Data scientists often exist in business settings and are charged with communicating complex ideas and making data-driven organizational decisions. As a result, they must have effective communication and leadership skills.

A Skills Metrix is a smart approach to combine technical and interpersonal skills in a neatly structured, eye-grabbing display.

Professional Skills Matrix
Technical CompetenciesInterpersonal TraitsTech Skills
Data AnalysisCommunicationR Programming
StatisticsCreative ThinkingNoSQL
Data VisualizationCritical ThinkingHadoop
ProgrammingProblem SolvingOpenRefine
Quantitative AnalysisActive LearningTensorFlow
Machine LearningPerceptivenessMatplotlib
DebuggingGenerating HypothesesCloudera
ProbabilityInterpersonal SkillsVB

Qualifications/Certifications associated with Data Scientists

Introduction to Statistics CourseProbability CourseMicrosoft’s MCSE: Data Management and Analytics
Statistics and Data Analysis CourseAlgebra CourseMicrosoft Professional Program Certificate in Data Science
GCP—Google Certified Professional Data EngineerCloudera Certified ProfessionalCornell Data Analytics Certification (Online)
CHDA – Certified Health Data AnalystMapR Certified Data ScientistCAP – Certified Analytics Professional

Optional Extras for Data Scientist Resumes

If you are a graduate and just starting out as a Data Scientist, your resume may be lacking information. It can be beneficial to create another section to add optional extra experience to extend your resume.

Freelance Work

  • NCAAMaster: Beat 280+ students, and professors in an NCAA pool by finding the right data metrics for the job.
  • FootForestAIA: Used a random forest regressor technique to predict fantasy football scores.
  • Pylearn 2: Regular contributor to Machine Learning project on GitHub.


  • AAAI Conference on Artificial Intelligence (2017)
  • The Machine Learning Convention—Spoke on a Panel About AI (2016)


  • On a team that won the Kaggle “Titanic: Machine Learning” challenge.

Additional Activities

  • Article “Machine Learning in the Next 10Years” cited in Forbes.
  • Leader of a bi-monthly hiking club.

Professional information for Data Scientists

Sectors: Various
Career Type: Programming, Analysis, Statistics, Scientific, Data Mining, Research, Mathematics, Machine Learning
Person type:  Analyst, Developer, Programmer, Coder, Modeler, Surveyor Compiler, Investigator, Reporter
Education levels: Bachelor’s to Masters’ Degree
Salary indication: An average of $ 122 889 (Indeed)
Labor market: 27% growth from 2019 – 2029 (
Organizations: SME, Fortune 500, Government, NPO, Corporate, Commercial,  

Data Scientist Resume Example Downloads