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Data science is one of the most revolutionary technologies helping businesses grow rapidly worldwide.

More than 105,980 data scientists are currently working in the United States at an average annual salary of $108,660.

These statistics might fascinate you to become a data scientist in 2022, and you must start learning that. However, you will need a professional data science resume after learning data science by any medium.

In this blog, we will elaborate on all the essential points to build an impeccable data scientist resume.

Table of Content:

What does a Data Scientist Do?


The primary job of a data scientist is to crunch raw data and turn them into meaningful insights that an organization needs to make valuable business decisions.

They are also responsible for developing data modeling processes & creating statistical models to crunch business data and analyze it to find meaningful insights.

Here are the typical roles and responsibilities of a Data Scientist:

  • Coordinate with the stakeholders & understand the business requirements
  • Acquire data from the clients & sort the data based on various criteria
  • Integrate data from different data points to initiate data investigation
  • Apply data science techniques including statistical analysis, AI, and machine learning to analyze data
  • Use Data Visualization techniques to measure results and present results to the stakeholders.

Data Scientist Salary


Robert Half Technology’s 2020 Salary Guide says that the average salary of a data scientist is between $105,750 and $180,250 per year in the USA.

However, the salary ranges from place to place.

Here are Data Scientist Salary for cities in the USA:

Best Resume Format for a Data Scientist Resume


When it comes to a data science resume format, what all options do you have?

You have three. Let's talk about the pros and cons of each.

Reverse Chronological Data Scientist Resume Format


The most conventional resume format in the market, and a darling of the recruiters, the reverse chronological resume format is the best bet for a resume of a data scientist.

As the name suggests, this resume format entails listing all your experiences and qualifications in reverse chronological order. This means listing out your most recent qualification/experience first, followed by the previous ones, until you reach your trajectory's beginning.

Sections in a Reverse Chronological Resume Format

A data analyst resume in a reverse chronological resume format contains the following sections:

  1. Header
  2. Personal Details
  3. Title
  4. Professional Summary
  5. Technical Skills
  6. Key Skills
  7. Professional Experience
  8. Education
  9. Certification, Conferences, and Publications
  10. Additional Information

It's always better to keep the target profile in mind when you're sending across your data scientist resumes.

Also Read: What are the different sections to add to a resume?.

Pros of a Reverse Chronological Resume Format


Given below are some of the pros of a reverse-chronological resume format.

1. ATS Optimized


The reverse chronological resume format is ATS-friendly and is the industry standard for resumes. This format will guarantee that an ATS effectively parses your resume.

2. Preferred by Recruiters


Since the ATS can effectively parse resumes in the reverse-chronological format, it is preferred by recruiters as well. This format allows the hiring managers to quickly scan the resume for information they think is most relevant, thus saving valuable time.

3. Ease of Preparation


If you are a data scientist making your resume for the first time, or if resume writing is not your most preferred way of spending time (trust us, we understand!), you can simply opt for a reverse chronological resume format.
You need to create a timeline of your professional trajectory and expand each item in your resume.

4. Prioritizing your most recent work experience


This format allows you to emphasize your most recent work profile. Consequently, your earlier roles (unrelated to your target profile) will be sidelined in place of your most recent professional profiles.

Cons of a Reverse Chronological Resume Format


Given below are some of the cons of a reverse-chronological resume format.

1. Gaps in your career


If there are gaps in the professional trajectory, this resume format will make it evident to the recruiter. Since the dates are mentioned for every qualification and experience, not only will the recruiter notice that, but the ATS might also disqualify you (in case you lack the required work experience).

2. Frequent job switches


If there are a lot of companies across a limited time frame, this might signal that you are an unstable employee who might not stick around for long.

Functional Resume Format for Data Scientist Resumes


At the other end of the spectrum lies the functional resume format. Here, only the professional headers are mentioned, without diving into the details of each work profile.

Instead, a separate section is created along with the Summary of Skills, wherein the relevant points are grouped under the appropriate skills.

This resume format is ideal for those with many gaps in their professional trajectory or those who have a history of frequently switching entry-level data science jobs.

Following are the pros and cons of using the functional resume format:

Pros of a Functional Resume Format


Given below are some of the pros of a Functional resume format.

1. Covers employment gaps


Since this resume format focuses on skills over the actual professional trajectory, this format is suitable for those professionals who have a lot of gaps in their careers.

2. Addresses the issue of job switches


The emphasis on skills allows the applicant to focus on skills, thus allowing the recruiter to gloss over the factor of frequently switching jobs.

Cons of a Functional Resume Format


Given below are some of the cons of a Functional resume format.

The functional resume format is not:

  • ATS-friendly
  • Suspicious to recruiters
  • It has no focus on the career trajectory

Combination (Hybrid) Data Science Resume Format


The combination resume format or the hybrid resume format combines both reverse chronological resume and the functional resume.

The first half of the combination resume is written using the functional format. So, the first half focuses on and highlights a person's skills and accomplishments.

The second, the combination resume, is written in the reverse chronological order, which means the professional history of a person is written in the reverse chronological order.

Pros of a Functional Resume Format


  • Provides the information in reverse chronological order
  • Highlights the skills

Cons of a Functional Resume Format


  • It Consume more time to create
  • The functional resume may not comply with the company-specified guidelines

Also Read: What format to use in a resume in 2022?.

3 Stages of Writing a Data Scientist Resume


We've divided the data science resume into three stages to make the data scientist resume-making process more straightforward. These three stages are:

  1. Master Data Scientist Resume
  2. First Draft Data Scientist Resume
  3. Final Data Science Resume

Also Read: How to write a perfect resume header in 2022?

Step 1: Create a Master Data Scientist Resume:


The first step is to create a master data scientist resume that includes all of your relevant skills and experience from previous jobs and internships.

For example, if you have extensive experience in one field, it may be difficult to recall all important skills and achievements when you create the final data scientist resume.

So, it's better to create a Master resume, which will consiste all your professional experience and achievements in reverse-chronological order.

Then, use this template to create versions tailored for each job application at hand. This way, you'll spend less time updating your resume every time you apply for a new position — and more time actually getting interviews!

First Draft for your Data Scientist Resume:


Next, create the first draft of your data scientist resume by focusing on the
job requirements of the job you're applying for.

For example, if you are applying for a business analyst role at ABC Inc., make sure to add relevant skills and experience from your master resume to the first draft resume, and leave off the unnessary skills and experiences.

In this stage, you will make the sections of the header, personal information, title, education, certifications, conferences, publications, and the section of additional information.

Pro-Tip: After making your master data scientist resume pdf, save a copy. It will be of a lot of help in the future when you need to update your data scientist resume as you change your career or switch to a new job.

Final Data Scientist Resume:


In this stage, you need to do the following three things:

First, draft the skills section.

  • Pick out all the skills that you acquire from your professional experience section and the job description, then write them in the key skills section of your data scientist resume.

Second, compose your data scientist resume summary/data scientist resume objective section.

  • Look for the points that highlight your career in your professional experience section and write them in the data scientist resume summary/data scientist resume objective section of your data science resume by rephrasing them a little.

Pro-Tip: Professionals with 3+ years of experience will make a data scientist resume summary section, whereas entry-level professionals will make a data scientist objective section.

  • Highlight and bold all the essential words, phrases, and numbers in the sections of professional experience, certifications, conferences, publications, and the additional information section.

Also Read: How to becoma a Data Scientist in 2022?

Writing Data Science Resume Header


A header comes at the top of your data scientist resume.

It consists of four important elements:

  • Resume title
  • Contact Information
  • Location
  • LinkedIn or any social media profile link(optional)
  • Profile Designation

Data Science Resume Title


Given below are some tips to write the Resume Title:

  • Do not write "CV" or "Resume" as the resume title. Instead use your full-name as the resume title.
  • Give a single space between the first and the last name
  • Keep the font size between 16-18 points when writing the title of the resume
  • If you have a middle name, write the initial of your middle name on resume title. For example: if your name is Sheldon Lee Cooper, write Sheldon L. Cooper in the resume title.

See the data scientist resume sample given below to see how an on-point data science resume title should look like:

Title section in a Data Scientist resume

Personal Mobile Number in Data Science Resume


Write only one mobile number on which you are available 24x7.

The two rules that you need to follow while writing your mobile number are:

  1. Write your country's International Subscriber Dialing (ISD) code followed by a plus sign (+) before your mobile number.
  2. Give a single space after the first five digits of your mobile number.

Writing only that number on which you are 24x7 available is essential as a personal mobile number is a primary source through which a recruiter is likely to contact you.

Personal E-mail ID in Data Science Resume


Write only that e-mail ID in your information section, which you often use if you have multiple e-mail IDs. And keep the email ID formal and professiona.

For example: Use email Ids like:

john.doe@email[dot]com
doe.john@email[dot]com
john.doe.22@email[dot]com
doe.john.21@email[dot]com

Avoid email Ids like:

rockingjohn1992@email[dot]com
lannisterfanboy101@email[dot]com

Pro-Tip: Do not include personal information such as marital status, age, political views, etc. They are not to be mentioned in a data science resume.

Current Location in Data Science Resume


  • This is the third thing to include in the resume heading section. Here you will write the current location of your residence.
  • You do not need to get into the details of your residence. For instance, No need to mention the exact address of your home.
  • If you're applying for a job in your own country, write the location in City/State Code format.
  • If you're applying for a job in another country, write the location in city/country code format

At this section, you can add hyperlinks to your social media websites like LinkedIn, Facebook, Instagram, etc., and personal websites or portfolios if you have any if they provide any support for the professional work that you have done so far.

If you have live links to your projects, or Github repository, you can add those in your resume to help the recruiters understand your work experience.

Take a brief look at the data scientist resume sample below to get a better understanding of how to write the personal information section:

Data-Scientist-resume-personal-information

Also Read: How to write the contact information in resume in 2022?

Data Scientist Resume Profile Title


Next in line comes the profile title section.

The profile title tells the recruiter the level at which you carry out your job responsibilities and duties.

The title is supposed to be the second-largest text in your data scientist resume, written in between the font sizes of 12-14 points.

Writing a profile title makes the recruiter's job easy and is a deciding factor for the recruiter to read your data science resume further or not.

Look at the data scientist resume sample given below to get a better idea about how a data science resume profile title looks like:

Data-Scientist-resume-personal-information-Profile-Title

Data Scientist Resume Skills


Writing the skills section in the final draft stage will give you more skills to write in your data scientist resume than you thought you had. Before writing your skills section, scan the rest of your data science resume once, thoroughly to look for skills.

Look at the below-given data science resume example to see how to write a key skills section:

Data-Scientist-resume-Key-Skills

Technical Skills Section of Data Scientist Resume


If you have skills like that of SciKit-Learn, NumPy, or SciPy, then do not include them in the key skills section. These are considered to be technical skills, so make a separate section for them named 'Technical Skills Section.' It will be made in the same way that you have made your key skills section.

List of Skills to Add for a Data Scientist


| Key Skills | Technical Skills |
|||
| Data Science | Python |
| Data Analysis | R |
| Machine Learning Algorithms | Hadoop |
| Big Data | Tableau |
| Predictive Models | Analytics |
| Data Visualization | Tensorflow |
| Neural Networks | Java |
| Natural Language Processing | Scala |
| Logistic Regression | Pandas |
| Exploratory Analysis | AWS |
| Data Quality | Scikit-Learn |
| Data Extraction | Matlab |
| Sentiment Analysis | ETL |
| Business Process | Nosql |
| Cluster Analysis | Unix |

To get a better idea, take a glance at the data scientist resume sample given below:

Data-Scientist-resume-Technical-Skills

Also Read: How to write the key skills section of a resume in 2022?

Data Scientist Resume Summary


Your recruiter resume summary is where you can clearly communicate the value that you bring to a company and what you have achieved in previous roles.

Here are some tips to write the resume summary section of data scientist resume:

  • If you have 3+ years of experience, then only add the summary in the resume.
  • Keep the resume summary betwieen 2-3 lines.
  • Add the most important and relevant skill in the resume summary, just to get the recruiter's attention.

Look at the data scientist resume sample below to see how to optimize your data scientist resume summary section:

Data-Scientist-resume-Summary

Also Read: How to write a resume summary in 2022?

Data Scientist Resume Professional Experience Section


A professional experience section includes all the professional work that one has done in their career.

This section holds a lot of importance for professionals with extensive career history.

You can mention the following details while framing the professional experience section of your data science resume:

  • Profile Name
  • Company Name
  • Company Location
  • Serving Period
  • Highlights of Your Activities

Data Scientist Resume Professional Experience Example


Data Visualization & Predictive Analytics

  • Steering rapid model creation in Python using Pandas, NumPy, SciKit-Learn & plot.ly for data visualization
  • Creating NLP models for Sentiment Analysis & MapReduce modules for predictive analytics in Hadoop on AWS
  • Deploying ridge regression model & LASSO solver via gradient descent to select the regularization parameters
    Statistical Modeling & ML Algorithms
  • Applied various machine learning techniques to build dynamic pricing models and maximize profitability
  • Led the development of a performance assessment & pricing analysis platform by deploying k-NN Algorithm
  • Created multivariate regression based attribution models using ad stock analysis from the digital marketing data

What to Do If You Don't Have Professional Experience in Data Science?


If you don't have professional experience in data science, you may be wondering if it's possible to land a job without having a background. The answer is yes, but it's not going to be easy.

Regardless if you have experience or not, recruiters will want to see that you have the skills to become a data scientist that they can trust.

So, it's extreamly important that you've done some work in the field.

If you alreay have some works in data science filed, add these works in your resume.

If you don't have any experience yet, here's some dea you can start with.

1. Do Some Personal Projects


This could be anything from looking at your own spending habits to analyzing popular products on Amazon or Reddit.

You can also look at open source datasets online and try building models with them or find other interesting projects to try out on your own. These personal projects will give you hands-on practice with machine learning and help build up your portfolio.

2. Get Involved in Kaggle Competitions


Participate in data science competitions online, like Hackathons and Data Science Competitions (e.g. DSC). These are great ways to gain skills and aexposure because they're judged based on algorithms rather than coding skills.

And participating certification of these competitions are extreamly valuable when applying for a job.

Action Verbs for Data Scientist Resume


Here are some of the action verbs you can use in the data scientist resume:

  • Analyzed
  • Demonstrated
  • Initialized
  • Initiated
  • Spearheaded
  • Formulated
  • Conceptualized

Look at the data science resume example below to see how a professional experience section should look like:

Professional Experience section in a Data Scientist resume

Also Read: How to write the work experience section of a resume in 2022?

Data Scientist Resume Internship Section


While still in college, students are likely to do a maximum of two internships. These internships help you gain professional experience and hands-on professional knowledge of the workings of your chosen field. So, that is why the internship section holds a lot of importance in a freshers data analyst resume.

Including internships in your data scientist resume will give you the upper hand over the freshers who haven't done any internships during their graduation.

Use the following format to write your internship section:

{Name of the Organization} | {Location} (city, country pin) | {Dates} (in mm/yy-mm/yy format) | {Designation}

This section of the internship will also be written using the cause-effect method along with grouping & highlighting, as explained in the professional experience section above.

Also Read: What are the popular Data Science career options in 2022?

Education Section in the resume of a Data Scientist


The education section is one of the essential sections of a recruiter's data science resume. It helps the recruiter decide whether you are eligible for the job.

Mentioned below are the elements you need to include in the education section of a data scientist:

  • Name of the university you have attended
  • Name of the courses you have pursued
  • Location of your school
  • Enrollment and graduation dates in the month and year format

The data scientist resume sample provided below will give you a better idea of how to write the education section:

Data-Scientist-resume-Education

Also Read: How to write the education section of a resume in 2022?

Certification Section in the Resume of a Data Scientist


The second last section in the first draft stage is the certifications, conferences, and publications section.

In this section, you will write all the certifications, conferences, and publications that you ever got, attended or got published. Also, these should be those certifications, discussions, and magazines that will add some value to your data scientist resume.

Use the format given below to write your certifications.

{Name of Certification} | {Affiliating Institution} | {Location} | {Date (month & year)}

To write your conferences, use the below-given format:

{Your Role in the Conference} | {Title or Topic of Discussion} | {Conference/Forum Name} | {Date (in mm/yy) and Location}

Use the format given below for writing your publications:

{Authors Name} | {Title of Article/ Chapter} {Name of journal, website, magazine, etc.} | {Year of Publication} | {Publisher or Issue Number} | {URL if its an online publication}

See the below-given data scientist resume example to see how the certifications, conferences and publications section should be like:

Data-Scientist-resume-Certification

Also Read: How to write the certification section on a resume in 2022?

Data Scientist Additional Information Section


The last section to be made in the first draft stage is the additional information section.

In this section, you will write additional information about the languages you know. For example, if you are fluent in speaking and writing English and Spanish, you will mention it in this section.

Also, you will make this section of additional information only if you know how to speak and write more than one language.

To get a more clear idea on how to write this section, look at the data scientist resume sample given below:

Data-Scientist-resume-Additional-Information

Data Scientist Resume Sample

If you follow all of our tips, you can achieve an industry-standard data science resume like the data scientist resume sample given here!

Leonard Hofstadter
Data Scientist
SUMMARY
6+ years experienced data Data science professional adept at developing analytic models to process raw data for identifying product improvement opportunities. Gained an understanding of deploying machine learning modelling algorithms for recognizing customer sentiments as part of enhancing customer satisfaction.
KEY SKILLS
• Data Analysis • Predictive Modeling & Analysis • Statistical Analysis • Data Visualization • Behavioral Analysis
• Price Analysis • Performance Assessment • Regression Analysis • Sentiment Analysis • Profit Maximization
• Data Processing • Strategizing • Process Improvement • Leadership & Training • Team Incubation
TECHNICAL SKILLS
  • Packages: scikit-learn, NumPy, SciPy, Plot.ly, pandas, NLTK, Beautiful Soup, Matplotlib, Stats Models
  • Big Data Stack: Hadoop, Apache, Pig, Python, PostgreSQL, AWS, Hive, MongoDB, MapReduce, Spark, Linux
  • Statics & Machine Learning: Liner & Logistic Regression, SVM, Ensemble Trees, Random Forests, Gradient Boosted trees
PROFESSIONAL EXPERIENCE
Data Scientist
Positronix Financial Services
Start typing, then use the up and down arrows to select an option from the list
    It is an AI based company delivering solutions to 1k+clints across the globe with an employee base of ~2500 professionals
    Technology Stack: Python, Hadoop, AWS, pandas, NumPy, scikit-learn, plot.ly
    Key Achievements
    • Received Best Employee Award in the year in '20 for showcasing due diligence for work
    • Established the data science division from scratch by recruiting, on-boarding & training a team of 8 data analysts
    • Formulated clustering & regression analysis to resolve a shipping consolidation issue & reduce costs by USD 3 million
    • Migrated data transformation processes on Hadoop to reduce data processing time by 25% & cut costs by USD 550k
    • Developed a customer segmentation algorithm via Python to boost sales leads & increase market share by 28%
    Data Visualization & Predictive Analytics
    • Steered rapid model creation in Python via Pandas, NumPy, scikit-learn & plot.ly for data visualization
    • Constituted NLP models for Sentiment Analysis & MapReduce modules for predictive analytics in Hadoop on AWS
    • Designed real-time contextual behavioral personalization system via econometric & ML to predict customer behavior
    Statistical Modeling & ML Algorithms
    • Placed various machine learning techniques to build dynamic pricing models and maximize profitability
    • Led the development of performance assessment & pricing analysis platform via k-NN Algorithm
    • Formed multivariate regression based attribution models via ad stock analysis from the digital marketing data
    • Generated segmentation models using K-means Clustering in order to discover new segment of users
    Consultant - Data Analytics Division
    Epiplace Solutions
    Start typing, then use the up and down arrows to select an option from the list
      With a presence in 10+ cities, Epiplace has delivered cost-effective IT solutions 1500+ clients pan United States
      Technology Stack: Python, pandas, scikit-learn, Matplotlib, Jupyter Notebook
      Key Achievements
      • Accorded with CEO Appreciation Award for outstanding performance in the year '18
      • Performed a key role in yielding a K-S static of 51.5 by developing a logistic regression model and an additive scoring model for QSM
      • Deployed SGD, Logistic Regression, Random Forest, SVM, etc. for classification models to boost average click rate by 34%
      Segmentation & Clustering
      • Applied large scale & low latency machine learning for non parametric models & high-dimensional data visualization
      • Utilized high-dimensional data sets from users, media agencies & 3rd party apps via PCA, LDA & Kernel Approximations
      • Created multivariate regression-based attribution models & segmentation models via K-means Clustering
      Data Analyst
      PeopleSoft
      Start typing, then use the up and down arrows to select an option from the list
        It is one of the largest software development companies in the world with a revenue worth ~USD 40mn.
        Data Analytics & Model Development
        • Directed model development, validation, testing and implementation of analytical products and applications
        ML Algorithms & Statistical Analysis
        • Stationed advanced text mining algorithms to identify search intent latent in individual keywords
        • Tested and implemented decision trees, random forests, and ensemble model via bagging and boosting
        • Employed Principle Component Analysis to analyze collinearity, and reduce the dimensionality of the dataset
        EDUCATION
        BS in Data Science
        University of California
        Start typing, then use the up and down arrows to select an option from the list
          • CGPA: 3.8/4.0
          CERTIFICATIONS, CONFERENCES & PUBLICATIONS
          • Certified Machine Learning Expert | OpenAI | Aug '17
          • Certified Expert Data Scientist | Stanford University | Jun '16
          • Speaker | Open Data Science Conference | SF, CA | Jan '16
          • Published - Modern Method of Dynamic Pricing for Hotels | The Data Science Journal | May '15
          VOLUNTEERING
          • Volunteer at Home Shelter| Jun '20 - Present
          • Member of Boys & Girls Club | Organized workshops on importance of education | Jun '13 - Aug '18
          ADDITIONAL INFORMATION
          • Languages: English (native), Spanish (working proficiency), and Catalan (fluent)

          Key Takeaways


          You can build a data science resume by referring to any online data science resume example. However, you might face issues without actionable steps and expert guidelines.

          Take a look at the essential points to remember while building your data scientist resume in 2022:

          1. Ensure that your data scientist resume summary does not exceed the limit of 3-4 lines.

          2. Mention only those skills and achievements which really make you stand out from the other applicants.

          3. Tailor your professional experience section according to the job description.

          4. Make the key skills section just after your data scientist resume summary, followed by the technical skills section.

          5. Add profile-centric keywords picked from the job description to parse through the ATS.

          These guidelines will help you craft an effective data scientist resume. In addition, you can check out Hiration's Online Resume Builder to minimize your resume building time.

          This is an AI-powered platform with 24/7 chat support to offer you a smooth operating experience. You can also leverage our career assistance at support@hiration.com.