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650%.

That's how much the demand for data scientists has grown since 2012. In the US alone, there are roughly 35,000 people skilled in data science, with hundreds of companies lining up to hire them.

According to LinkedIn's 2017 U.S. Emerging Jobs Report, data scientist has been ranked second in its Top 20 List of Emerging Jobs, with a 6.5x jump in growth in the 2012-2017 period, right behind Machine Learning Engineer.

With such astronomical growth in the demand for this role, you'd think bagging a data scientist role would be a piece of cake.

Well, think harder.

How do you think you'd go about getting a data science job? You think you just need to get a certification and you're done?

Well, there's one tiny step missing in that trajectory. The data science resume.

That singular piece of document which will make or break your chances of getting shortlisted. You might be able to process hundreds of TBs of data for gleaning valuable insights, but if your machine learning resume lacks the punch, it'll all be for nothing.

Fret not. The experts at Hiration have made hundreds of data science resumes, and we've helped professionals land those high-paying gigs you read about in Amazon and Google. So trust us when we say that we know what we're talking about.

Our 2021 Guide to Data Scientist Resume will:

  • Since data science is a technical job, a technical skills section is a must for data scientist resumes
  • Along with LinkedIn, add a GitHub link with your live projects in the Data scientist resume
  • A bachelor degree in Computer science, Social sciences, Mathematics or Statistics in necessary to become a data scientist. Include your degree qualification in the resume education section.
  • Use single line bullet points to write the professional experience section of the data scientist resume.

Our in-house resume experts have created a state-of-the-art online resume builder containing ready-to-use content templates and ATS-friendly resume designs to give you an edge over others!

Here are a list of things you will learn from this blog:

Data Scientist Job Description

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

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

Here are the typical roles and responsibilities for 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 visulaization 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 scientists is between $105,750 and $180,250 per year in the USA.

However, the salary ranges from places to places.

Here are Data Scientist Salary for cities in the USA:

Why Do You Need a Data Scientist Resume?

Now that you broadly understand the demand for data scientists, and how it's only set to go up in the next 5-10 years, let's talk about data science resumes.

You'd think that with such an incredible demand, all you need to do is just walk in and you'll be greeted with open arms, right?

Well.

Here's the thing. Data is the new currency. Most companies realized it before it became a buzzword. And data scientists (along with ML/AI professionals) are going to rule the job market in the foreseeable future.

And you know that? Data scientists know it all too well.

While the demand is rising as we speak, the market has been flooded with data science professionals, with only a few certifications in their name.

These online courses sure add a lot of value, but the recruiters now have a new problem: how to sift through all the applicants and filter out the best ones?

That's where your data scientist resume comes in.

A data scientist resume is an opportunity for the applicant to stand out among other candidates.

A data science resume will give the recruiter an idea around your skills-sets and achievements.

Now that we know the importance of a resume of a data scientist, let's waste no further time and dive into the nitty-gritty of a resume for data science.

Additionally, you'll find data scientist resume sample in the end to provide further clarity on how a final data scientist resume should look like.

Look at the below-given data science resume sample to see how a finished resume of a data scientist looks like:

Data Science Resume Sample

6+ years experienced data scientist with a passion to solve real-world business challenges using data analytics. Track record of setting up the Data Science Div. for a leading hospitality firm & rendering consultancy services for a Fortune 500 company. Proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.
• Data Analysis • Stakeholder Management • Leadership & Training • Strategy • Project Management & Delivery
• Process Improvement • Team Incubation • Data Visualization • Predictive Modelling & Analysis • Sentiment Analysis
  • Packages: SciKit-Learn, NumPy, SciPy, Plot.ly, Pandas, NLTK, Beautiful Soup, Matplotlib, StatsModels
  • Big Data Stack: Hadoop, Apache, Pig, Python, PostgreSQL, AWS, Hive, MongoDB, MapReduce, Spark, Linux
  • Statistics/ML: Linear/Logistic Regression, SVM, Ensemble Trees, Random Forests, Clustering, Gradient Boosted trees
PROFESSIONAL EXPERIENCE
    Technology Stack: Python, Hadoop, AWS, Pandas, NumPy, SciKit-Learn, plot.ly
    Data Visualization & Predictive Analytics
    • Steered rapid model creation in Python using 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 a performance assessment & pricing analysis platform via k-NN Algorithm
    • Formed multivariate regression based attribution models using ad stock analysis from the digital marketing data
    • Generated segmentation models using K-means Clustering in order to discover new segments of users
    Key Achievements
    • 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 using Python to boost sales leads & increase market share by 28%
      Technology Stack: Python, Pandas, NumPy, SciKit-Learn, Matplotlib, Jupyter Notebook
      Segmentation & Clustering
      • Applied large scale & low latency machine learning for non-parametric models & high-dimensional data visualization
      • Created multivariate regression-based attribution models & segmentation models using K-means Clustering
      • Utilized high dimensional data sets from users/media agencies/3rd-party apps via PCA, LDA & Kernel Approximations
      Key Achievements
      • Developed an additive scoring model for QSM and a logistic regression model to yield a K-S statistic of 51.5
      • Deployed SGD, Logistic Regression, Random Forest, SVM, etc. for classification models to boost avg. click rate by 34%
        Data Analytics & Model Development
        • Directed model development, validation, testing and implementation of analytical products and applications
        • Developed an additive scoring model for QSM & a logistic regression model which yielded a K- S statistic of 51.5
        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 models using bagging and boosting
        • Employed Principle Component Analysis to analyze collinearity and reduce the dimensionality of the dataset
        EDUCATION
          Enter text here..

          • Languages: English, Spanish and Catalan
          • Awarded the “Best Employee Award" | Positronix Financial Services, '17
          • Received the 'CEO Appreciation Award' | Epiplace Solutions, '16
          • Certified 'Machine Learning Expert', OpenAI, '17
          • Certified 'Expert Data Scientist', Stanford University, '16
          • Speaker, Open Data Science Conference, San Francisco, '16
          • Published: “Modern methods of dynamic pricing for hotels”, The Data Science Journal, '15

          Data Scientist Resume - Best Resume Format for a Data Scientist Resume

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

          Broadly speaking, you have three. Let's talk about the pros and cons of each.

          Reverse Chronological 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 out all your experiences and qualifications in reverse chronological order. This means listing out your most recent qualification/experience first, followed by the previous ones, till you reach the beginning of your trajectory.

          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.

          Pros and Cons of a Reverse Chronological Resume Format

          While the reverse chronological resume format is the most preferred resume format, it's important to understand if it'd be suitable for you as an applicant or not.

          PROS:

          • ATS Optimized
            The reverse chronological resume format is ATS-friendly and is the industry standard when it comes to resumes. Going for this format will guarantee that your resume is effectively parsed by an ATS.

          • Preferred by Recruiters
            Since the ATS is able to 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 which they think is most relevant, thus saving a lot of valuable time.

          • 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.
            All you need to do is create a timeline of your professional trajectory and expand each item in your resume.

          • Prioritizing your most recent work experience
            It'd be very rare if you land a data scientist gig right out of college. Usually, a data scientist requires a certain level of experience. You don't necessarily need an IT background for pursuing data science as a career.

          But you don't want the recruiter to go through your earlier profiles. What do you do then?

          Enter the reverse chronological resume format. This format allows you to put more emphasis on your most recent work profile. Consequently, your earlier roles (which are not related to your target profile) will be sidelined in place of your most recent professional profiles.

          CONS:

          • 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).

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

          Functional Resume Format for Data Scientis 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 the lines of Summary of Skills, wherein the relevant points are grouped under the appropriate skills.

          To clarify, the experts at Hiration don't particularly recommend this resume format. You can take a look at the data scientist resume pdf available on the Hiration platform for better clarity. That being said, this resume format is ideal for those who have a lot of 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:

          • 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 gap in their career.

          • Addresses the issue of job switches
            The emphasis on skills allows the applicant to put the focus on skills, thus allowing the recruiter to gloss over the factor of frequently switching jobs.

          CONS:

          The functional resume format is not

          • Not ATS-friendly
          • Suspicious to recruiters
          • It has no focus on the career trajectory

          Combination (Hybrid) Resume Format

          The combination resume format or the hybrid resume format is a combination of 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 and highlights a persons skills and accomplishments.

          The second of 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:

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

          CONS:

          • Consume more time
          • Might not comply with company-specified guidelines

          Writing Your Data Scientist Resume

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

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

          Master Data Scientist Resume:

          Making the master data science resume is the proprietary method used by experts at Hiration. Doing so ensures that we have all the data required for making a data scientist resume pdf.

          This makes the data scientist resume making process easier going forward. A master data scientist resume basically has all the information of the candidate starting form there projects, academic experience, volunteer experience and professional experience.

          It is of a lot of help even in the future, when you need to update your recent data science resume in case of a job change or career change.

          This act as a database for all your experieces and help you create customized resumes for differnet jobs including entry level data science jobs.

          But at the master data scientist resume stage, you don't have to decide what all information will go in the final data scientist resume and which will not.

          The idea of a master data scientist resume is just to collect all the information at a single place.

          For example, let's say you are responsible for building and optimizing classifiers, data mining, steering data collection methodologies, data processing, conducting ad-hoc analysis, creating automated anomaly detection systems, etc.

          Now, if you directly jump on making a final data scientist resume, there are high chances that you might forget to include some of the duties which you performed.

          In such a case, master data science resume will help you as it will have all of the duties and responsibilities which you performed throughout your tenure in that company.

          The master data scientist resume works like an outline of your final data science resume. So, since you already have all the information related to your data scientist resume there in the master machine learning resume, you can tweak your final data scientist resume according to the information in the master data science resume.

          This process of making a machine learning resume will ensure to get you more interviews than you expect that you can get.

          After you've compiled all the information in the master data science resume, you will start off with making your professional experience section.

          First Draft for your Data Scientist Resume:

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

          Hiration Pro-Tip: After making your master data scientist resume pdf, do save a copy of it. It will be of a lot of help in the future when you'll 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 machine learning resume.

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

          • Look for the points that are the highlight of 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.

          Hiration 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 important words, phrases and numbers in the sections of professional experience, certifications, conferences and publications as well as the additional information section.

          Now, let us see in detail, how to write each section.

          Data Scientist Resume Professional Experience Section

          A professional experience section includes all the professional work that one has done in his/her career.

          This section holds a lot of importance for those professionals who have extensive career history.

          Use the below given format to write the professional experience section of your data science resume:

          {Designation} | {Organization} | {Location (city, country pin)} | {Dates} (month & year)

          Framing points in your Data Scientist Resume

          The above-given format will be followed by cause-effect points. Most people are able to tell what they did as Data Scientists, but they are unable to explain the impact that their work had on the business.

          So, that's when cause-effect points come in. Cause-effect points are what demonstrates the impact of a persons works on the business. Let us see how to write the cause-effect points with the help of data science resume examples.

          Data Scientist Resume Example 1:

          • Steering rapid model creation in Python
          • Built dynamic pricing models
          • Creating NLP models
          • Led the development of a performance assessment & pricing analysis platform
          • Deploying ridge regression model & LASSO solver to select the regularization parameters
          • Created multivariate regression based attribution models

          The points in data science resume example one doesn't have the method of cause-effect used in them, hence they don't provide much information and the impact that the person had on the business.

          Data Scientist Resume Example 2:

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

          Now, by seeing data science resume example 2, you can see how using the cause-effect method to write the data science resume points is providing much more information around the impact that your work had on the business.

          Grouping & Highlighting in your Data Scientist Resume

          The other elements that help bring out the best in cause-effect points are grouping & highlighting.

          Let us now see with the help of data scientist resume examples, how grouping & highlighting makes a difference in the data scientist resume points.

          Data Scientist Resume Example 1:

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

          There is no use of bucketing and bolding in the points of data analyst resume example 1, which help in increasing the readability of the content.

          Data Scientist Resume Example 2:

          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

          In data scientist resume example 2, we have made buckets of the skills used in doing the work as well as have grouped the points of works in which the same skills are being used.

          Like we've made the bucket of 'Data Visualization & Predictive Analytics' and have written all the points pertaining to these skills under that bucket.

          Same goes for the bucket of 'Statistical Modeling & ML Algorithms'.

          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

          Data Scientist Resume For Freshers

          As freshers in the field of data science do not have relevant experience, they can't make a professional experience as they won't have anything to write in it.

          So, now the question is: What will they make or do?

          The answer is: Internship Section. Yes. Freshers in this field will make an internship section.

          Internships are when you work in an organization as an intern to gain professional knowledge of your field while still not being a graduate, whereas, a proper job is when you work for an organization after graduating from college.

          While still in college, students are likely to do a maximum of two internships. These internships help you gain the 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 machine learning resume will give you the upper hand over the freshers who haven't done any internships during their graduation.

          Having done internships shows the recruiter how serious you are when it comes to your professional life and that they are hiring a professional who takes his/her career seriously.

          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 bucketing and bolding as explained in the professional experience section above.

          Writing Data Scientist Resume Header

          Now, that the master data science resume stage is over, let's proceed with the first draft machine learning resume. Start off this stage by making the data science resume header.

          You must be wondering: What is a header and why is it important?

          Well, a header comes at the top of your data scientist resume and it is made up of your name. Making a header ensure that your machine learning resume is being separated from hundreds of other data science resumes that have come in for the same profile.

          Also, it gives your data science resume an individual identity of its own and prevents from being misunderstood as someone else's machine learning resume.

          When writing your data science resume header, make sure that you give a single space between your first name and your last name. Spacing errors like that of double space or no space have a negative impact on recruiters mind. It will portray your carelessness when it comes to small details.

          If you have a middle name, then write only the first initial of your middle name and then put a full-stop after it. Also, give a single space after the full stop and then write your surname. In addition to this, do not write your nickname in the header. Don't write something like "Cara 'Nitro' Delevingne".

          Just simply write 'Cara J. Delevigne'.

          When making your data scientist resume header, the ideal size of its text is between 14-16 point size.

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

          Header section in a Data Scientist resume

          In addition to this, you can instead use Hiration's Online Resume Builder to make your data scientist resume and that header absolutely hassle free. Our Online Resume Builder uses a pre-set, fixed font size to make the resume header.

          Personal Information Section In Your Data Scientist Resume

          In this section, three things are written: 1. Personal Mobile Number 2. Personal e-mail ID and 3. Current location

          Contact details in Data Scientist Resume

          Personal Mobile Number

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

          Also, there is a proper way in which one needs to write the mobile number. There are certain rules to follow when writing your personal mobile number. You can't just go and scribble down that mobile number of yours.

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

          1. Write the International Subscriber Dialing (ISD) code of your country followed by a plus sign (+) before your personal 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 important as a personal mobile number is the primary source through which a recruiter is likely to contact you.

          Personal E-mail ID

          Write only that e-mail ID in your personal information section which you use most often in case you have multiple e-mail IDs.

          At this stage of data scientist resume making, you can even add hyperlinks to your social media websites like that of 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.

          Also, all the websites need to be up-to-date in terms of information and the information should be consistent across the websites.

          Hiration 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.

          The e-mail is the second way through which the recruiter might contact you. So, it is important to write only one e-mail ID that you use the most.

          Current Location

          This is the third thing to include in the personal information section. Here you will write the current location of your residence.

          When writing the current location of residence, first write the city name followed by the country pin.

          As a data scientist, every candidate must have experience with conducting data science projects at university or as part of their professional experience. These projects gives an excellent idea of the candidate's skills and experience.

          Include these project links and Github link in your resume to help the recruiters understand your work experience.

          Personal Information
          Contact Number +1 222 235 3531
          Email Address johnmiller@xyz.com
          Current Location Los Angeles, CA
          Github www.github.com/john

          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

          Customizing 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. It also gives a broad level idea of your level of proficiency in your field.

          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.

          If a machine learning resume is missing a job title, then it is missing a crucial part of the data scientist resume. Hence, it is incomplete. If you send your data science resume without a job title, then it will get difficult for the recruiter to gauge for which profile you've applied for.

          Writing a profile title makes the job of the recruiter easy and is a deciding factor for the recruiter to read your machine learning resume further or not.

          Look at the data science 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

          Education Section in the resume of a Data Scientist

          The education section is one of the most important sections of the data science resume for a recruiter. It helps the recruiter to decide whether you are eligible for the job or not.

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

          Data-Scientist-resume-Education

          Certification Section in the resume of a Data Scientist

          The second last section to make 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, conferences, and publications that will add some value to your machine learning 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

          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 the additional information about the languages that you know. For example: If you are fluent in speaking and writing English and Spanish, then 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 science resume sample given below:

          Data-Scientist-resume-Additional-Information

          Data Scientist Resume Key Sections

          Illustrating Your Data Scientist Resume Skills

          In the third stage of making machine learning resume, which is the final draft stage, the first section that you will make is the key skills section.

          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 have. Before writing your skills section, scan the rest of your data science resume once, throughly, to look for skills.

          These skills will basically come from scanning the professional experience section.

          After you're done writing the key skills section, bold the whole section.

          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.

          So, just like the key skills section, after you're done making your technical skills section, bold the whole technical skills section and then in addition to it, italicize the whole section as well. Italicizing the whole section will separate it from the rest of the data science resume and will make it more clearly visible.

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

          Data-Scientist-resume-Technical-Skills

          In addition to this, if you use Hiration's Online Resume Builder to make your machine learning resume, then you'll have the options of adding bar graphs and pie charts to make your skills section.

          Look at the data science resume sample given below to see how you can demonstrate your skills using bar graphs and pie charts in your skills section:

          Data Scientist Skills Picture

          Data Scientist Resume: Summary

          The data scientist resume summary section is written at the very end of the data science resume making process so that you can refer the rest of the machine learning resume and pick out the points that are the highlight of your career and then add those points in the data scientist resume summary section after rephrasing them a little.

          You need to use the data scientist resume summary section wisely by telling the recruiter what you can do for the organization by explaining how you used your data scientist skills to benefit the previous organization.

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

          Data-Scientist-resume-Summary

          Furthermore, You can use Hiration's Resume Reviewing Service to get your data scientist resume professionally reviewed by the industry experts to check your data science resumes complete consistency. Or you can scan the available data scientist resume template.

          Data Scientist Resume PDF

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          Data Scientist Resume Doc

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          1. Reliability (achievements and skills backed with quantifiable values)
          2. Cause vs. Effect relationship
          3. Readability (to pass the 6-sec test)
          4. Achievement based points over responsibility based points
          5. ATS Compliance
          6. Compliance with industry norms
          7. Global compatibility
          8. Performance assessment (How well a candidate has performed to get shortlisted)
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          Key Takeaways For Your Data Scientist Resume

          1. Ensure that your data scientist resume summary does not exceed the limit of 3-4 lines. Write only those points in the data scientist resume summary section which highlights your most distinct skills and achievements across your career.

          2. Usually, the data scientist resume summary section is the first section that a recruiter sees. Therefore, it is important that it is crisp and includes only the most important point in it.

          You can begin the data scientist resume summary by writing the number of experience you hold and then mentioning your filed followed by your proficiencies.

          1. For example: '7+ years experienced data scientist with a passion to solve real-world business challenges using data analytics.' Continue in this manner and mention some special hards skills and some achievements that proved your proficiency in the previous organization.

          2. Since the space in the data scientist resume summary section is limited, mention only those skills and achievements which really make you stand out from the other applicants. And with this also ensure that the skills and achievements that you write in the data scientist resume summary section are relevant to your target profile.

          3. Tailor your professional experience section according to the job description. Make sure to mention the skills written in the job description in your machine learning resume.

          Also, if you have performed any of the duties written in the job description, then ensure that you write those duties in your professional experience data science resume points.

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

          Making the key skills section is of paramount importance as these days the resumes are first passed through the Applicant Tracking Systems (ATS) and after passing the ATS test, only do they get on the table of the recruiter.

          1. The ATS scans the resume for the keywords that are written in the key skills section. If the key skills section will be missing, then your resume will get rejected at the stage of ATS testing itself.

          Also, only write hard skills in the machine learning resume which you can substantiate in the interview and avoid writing soft skills.

          1. Include your key skills across your data scientist resume and not just in the key skills section. Writing keywords throughout the data science resume increases your chances of passing the ATS test.

          2. There are chances that the companies in which you have worked previously are not known to the recruiter. So include a one-line description of your company under the company name in your machine learning resume. In this description, try to sell the company to the recruiter.

          3. In this description, you can include some numbers around the number of employees the company had, its revenue, etc. Basically, anything that lets the recruiter know that you've been working for a good company. Do the same for all the companies that you've worked for.

          Still got more questions around your data scientist resume? Send your questions at team@hiration.com and our data scientist resume experts will get back to you.