Data science is one the most competitive fields in the modern job market, attracting aspiring specialists from all around the world. Becoming a data scientist means entering privileged circles with enticing career prospects and high salaries. The interest in this sphere is ever growing and the demand for data professionals is constantly increasing, yet the requirements candidates are expected to meet are also quite tough.
The first important step for a data science job applicant is to write an effective resume capable of impressing a hiring manager/recruiter enough to land you an interview. For those specialists who already have a lot of work experience in the field, this is a relatively easy task, while for entry-level data scientists it can be rather challenging and trigger a lot of questions: How can I make up for my lack of work experience? Is it a good idea to describe previous work experience and/or education if it is not completely relevant to data science? Which section should go first: education or experience? What kind of projects do I need to include, and how many of them? Should I list my soft skills? Should I add colors to my resume? All in all, how can I stand out from the crowd and get closer to the alluring goal of being employed as a data scientist?
In this article, we will discuss some essential tips and tricks on how to craft a compelling, professional, and easy-to-read data scientist resume that will draw the attention of any hiring manager or recruiter.
How to effectively organize your resume layout and formatting
Fit your resume on one page
Following this advice could be painful if you were planning on putting a lot of information in your resume. It may even seem counterintuitive; isn't it better to include as many details as possible to exhaustively describe to a potential employer all your experience, education, achievements, and skills in order to impress them? And if so, what if it takes up more than one page? Regardless, it is strongly recommended to always keep your resume just one page long.
Being able to expose your experience and accomplishments in a condensed form is a great way to show off your communication skills.
Select a resume template
While you can create your resume from scratch and organize it according to your tastes, you can save time by using an online resume builder and choosing from a variety of pre-existing resume templates. Alternatively, it is possible to select an appropriate free resume template from Google Docs or MS Word collections. Such templates are usually clear, visually appealing, well-formatted, legible, easy-to-use, and designed in a way to help you fit a lot of information in one page without looking cramped and overwhelming.
Here are some resume builders you may find useful:
Many resume builders, including the ones listed above, offer a wide range of both free templates which are sufficiently functional, as well as more advanced paid versions. When choosing a template for your data scientist resume, give preference to simple templates with just 1 or 2 colors in addition to black and white, rather than fancy and colorful ones. The latter is more suitable for other spheres such as art or design, where a creative-looking resume can impress an employer. In our case, it is better to avoid unnecessary decorations.
Consider choosing a two-column layout
Choosing a resume template with two columns (and maybe some narrow side columns for extras) helps use the page’s vertical space more efficiently. In this way, you can put more information in your resume and better spatially organize different sections.
Use efficient formatting
This set of tips will be of use if you decide to create your resume from scratch or introduce changes to a pre-existing template:
- Use an elegant text font
- Good examples: Calibri, Times New Roman, Arial, Verdana, Cambria, Tahoma, Georgia
- Bad examples: Comic Sans MS, MS Gothic, Ink Free, Agency FB, OCR A Extended
- Use a sufficient font size: 11-12pt for the text, 14-16pt for section headings and the header
- Make section headings and the header bold
- Avoid using too many text styles such as bold, italic, bold-italic and underlined
- Use 1-1.15 line spacing
- Avoid a boring black-and-white resume—add some color to make it stand out, but don't exaggerate, 1-2 colors will be enough
- Avoid visual effects, decorations, or unnecessary icons
- Use bullet points to make your resume look clean, well-organized, and easy to follow
Keep the format consistent
This refers to various stylistic features. For example, if you use bullet points in different sections throughout your resume, you should use a uniform style e.g., empty squares. The same can be said about indentation, font style, date format, etc. Fortunately, if you create your resume in an online resume builder, all these elements and formats are usually predefined for you.
General tips on your data scientist resume content
Create a master resume
Before starting to apply for data science jobs, it makes sense first to create a "master resume". This is a giant, very detailed version of your resume that can count 2-3 or even more pages, where you can include all of your work experience, studies, projects, technical and soft skills, and other achievements.
If you are a career changer trying to get into data science from another sphere, consider including experience from your previous jobs in your master resume; you will likely have more transferable skills than you realised. Not surprisingly, your master resume can have a lot of bullet points for each section or subsection. After completing it, you can easily use it as a basis for applications for various data science job positions; simply remove redundant details, sections, and bullet points from a copy of your master resume and adapt it for each submission.
Tailor your data scientist resume to each job description
This step is vital in your job application process. To increase the chances of getting your resume to stand out, you must customize it for each job position you apply for. A hiring manager usually receives numerous applications every day, so will likely have just a few seconds to scan each of them before deciding if they are worth more detailed reading or not. Hence it is crucial that your resume catches their eye.
The best approach here is to carefully read the job description and figure out the whole "wish list" and requirements that the company is looking for in a candidate. Based on these facts, incorporate and highlight these desired skills into your resume. It is essential to use the relevant keywords mentioned in the job description anywhere you can include them; this will help your resume get through any applicant tracking system (ATS) the company may use, and reach the eyes of humans for further consideration.
In addition to reading the job description closely, it is always a good idea to browse some information about the company itself. Explore the company's website, its mission, values, social media, products and services, so you can get a better understanding of what this employer is looking for in a successful candidate. Make your resume show that you are the perfect fit for this company, with all the potential to help the business grow. Each company looks not for the specialists who want any job, but for those who want namely this job.
Be concise but informative
Now that you know exactly what to include in your data scientist resume, you should rework and adapt this information to be laconic, precise, relevant, and well-presented. Don't include anything that might not add any additional value to this role and was not mentioned or implied in the job description. Keep your resume concise, purposeful, and informative. Any bullet statements should be brief and not span over several lines.
Follow reverse-chronological order
A reverse-chronological order is the most common and preferred format for the sections on education and work experience. The most recent experience should go at the top, followed by the second to the last, etc. This allows a hiring manager to quickly trace your professional growth and latest achievements.
Use plain but efficient language
Using simple and clear language is another way to demonstrate your communication skills. To do this efficiently, keep the following suggestions in mind:
- Don't overload your data scientist resume with technical jargon. While the job position for which you are applying may require a high level of technical skill and knowledge, remember that the first person who will read your resume will be a hiring manager or recruiter. Depending on who is head of recruitment in the company, they may have a different background and so could find too much technical jargon confusing. Hence make sure you write so that anyone could understand your potential value for the company.
- Use the job description as a guide; if it is filled with jargon, then it is ok for you to sound a bit more technical. Otherwise, try to include only major algorithms and techniques mentioned in the advert.
- Write in concise sentences.
- Keep the tense of your resume consistent.
- Avoid inflated words. Use help instead of facilitate, use instead of utilize, manage instead of administrate, complex instead of sophisticated, etc.
- When describing your work experience, projects, and achievements, use meaningful verbs. For example, instead of the verbs such as worked, made, or participated, use built, automated, optimized, etc. We will discuss this tip in more detail later in this article.
- Replace superlatives (highest, best, most important, etc.) and seemingly powerful but effectively not very informative adjectives like experienced, strong, considerable, efficient, with concrete metrics and results. Use these strong words (experienced, deep, proven, etc.) sparingly in your resume’s summary section.
- Ask a friend with a non-technical background to read over your data scientist resume. Ask them to give you feedback: Is it easy enough to follow? Is the language clear and simple? Have you managed to convey your key professional achievements?
Check for errors and typos
Apparently the lesser evil, silly errors and typos can make a negative impression on a recruiter and lead to them rejecting your resume. Double-check your grammar and spelling using a specialized online service (Grammarly or similar) and ask someone to check your resume for any typos or errors.
Send your data scientist resume to a real person
Before getting into the hands of a recruiter, resumes usually pass through a machine learning program called an applicant tracking system, or ATS, which decides if it is worth passing on to humans or should be rejected. To bypass this step, try to send your resume directly to a hiring manager, or even to the technical manager in charge of the position’s department.
How to arrange the overall structure of your data scientist resume
Include skills and projects sections
In recommended order from top to bottom, typical sections for a data scientist resume are:
- Contact Information
- Summary (or Objective)
- Work Experience (or just Experience)
- Additional sections
The order of these headings can be relatively flexible and is supposed to reflect the importance of each section, so the first ones should also take up more space than the last ones. In the case of a two-column resume design (the preferred one), remember that recruiters usually look through a resume from top-left to bottom-right. So, make sure you put the most relevant information at the top-left of the page.
Optimize the order of your sections
The best order of the sections, in particular, Work Experience, Projects, Skills, and Education, depends on 2 factors:
- Your actual experience. Here we can find different scenarios:
- If you have a long record of relevant work experience, prioritize the corresponding section in your resume, substantially reduce the information about your education and maybe even exclude your additional projects.
- If you are a fresh graduate with limited experience, put the education section first, add information about your final votes (of course, only if they are favorably high), and describe your course work or dissertation in more detail.
- If you have limited experience in data science and have worked mainly on freelance projects, interchange the sections Projects and Work Experience (or even combine them into one section Experience), include any data-related internships you have had (if any), and elaborate on your skills.
- If you are a career changer entering data science from another sphere, focus on Projects and Skills and write less on your work experience and education, although it is wise not to omit these sections altogether.
- The company profile and job description. Quite logically, if you are applying for a job in an academic environment, expand more on your education, certifications, additional courses, and scientific publications. Otherwise, expose more business-oriented experience and achievements. For start-up projects, in particular, the valuable qualities in potential candidates are the ability to work independently, being a self-starter, and having an innovative mindset. In this case, showcasing more individual projects can be an advantage.
Include additional sections in your resume if possible
For your data scientist resume, consider the following additional sections:
You can introduce any of them separately if you have at least two bullet points per section. For example, if you completed three data science courses and presented at two data-related conferences, it makes sense to add the sections Certifications and Conferences. Otherwise, if you have only one bullet point for some categories (e.g., one conference, one publication, and one hackathon), it is better to combine these achievements into one section called Extras.
Avoid verbose section subheadings
When naming the subheadings, be as laconic as possible:
- Relevant Work Experience Work Experience (or just Experience)
- Professional Experience Work Experience (or Experience)
- Notable Projects Projects
- Skills and Knowledge Skills
- Skills and Tools Skills
- Licenses and Certifications Certifications
- Additional Information Extras
How to correctly fill in your contact information
The most suitable place for your contact information is at the top of your resume, despite some online templates locating it at the bottom of the page (if this is the case for your selected template, just drag it manually to the top). The most crucial requirement for this section is the accuracy of the information.
Here is what you need to include in your contact information:
- Your full name.
- Your job title. Right under your full name, put the title of the job you are applying for rather than your current job title, whatever it is. You can copy the job title from the exact job description, or, if it is too long and specific (e.g., Data Scientist for Geoscientific Data Analysis and Webtool Development), cut it down to a more digestible form (e.g., Data Scientist).
- Your phone number. Needless to say, this should be your personal phone number, not the one from your current workplace.
- Your email. Put your professional-looking email address, which should be a combination of your name and surname: [email protected]. Don't put here a frivolous or inappropriate email that you may have for personal use, like [email protected]. If you don’t yet have a professional email address, make one.
- Your location. This is optional and doesn't need to include your whole address. City and state or country are enough.
- Clickable links to your active and updated profiles on LinkedIn, GitHub, Medium, Kaggle, etc. Most employers go here to check the extra information on a potential candidate, such as his/her portfolio of projects, articles on data science topics, and participation in hackathons. Instead of adding raw, clumsy and long links, consider inserting an icon of the corresponding website’s official logo and make it clickable, directing straight to your profile. You can search the official website logos on Google, here are the logo links for LinkedIn, GitHub, Medium, and Kaggle. Evidently, all the profiles that you decide to include in your data scientist resume should efficiently back up your data science skills and achievements. It doesn't make sense to showcase your LinkedIn profile if it refers only to your previous profession, or your Github profile if it doesn't contain any data science projects.
A couple more suggestions on the Contact Information section:
- Don't include the name of this section (unlike you would do for the other sections), just insert the information directly.
- Analogically, don't add the names of the categories (full name, phone number, etc.). For example, instead of Email: [email protected] simply write [email protected].
- Don't add your photo. While some resume templates provide this option, consider skipping it for your data scientist resume.
How to write an eye-catching data scientist resume summary or objective
Right after your contact information put the Summary or Objective section. These two sections are not the same, and you need to choose just one of them. A resume summary is a brief description (2-3 sentences, no bullet points) of your professional story, achievements, and qualifications. A resume objective is a brief statement (also 2-3 sentences, no bullet points) focused on your future career goals and the potential value you can provide for the employer.
These sections are vital if you are switching to data science from another field, since they explain in a condensed form why you are the perfect candidate for that data scientist job. Opt for Summary if you already have some experience in a data-related sphere. If instead you are a fresh graduate with very limited to no experience, choose Objective to demonstrate your passion for data science and willingness to be useful to the company.
A good resume summary or objective should:
- State your experience level (junior, senior, experienced, etc.) and the area of your expertise
- Mention the number of years (if any) you have spent in the field of data science
- Briefly and specifically summarize your skills and achievements
- Include information about your education and certifications
- Explain your move into the data science field, if this is the case
- Outline your long-term career goals
- Show your motivation and enthusiasm for the job and company
- Explain what value you can provide for the company
You need to select the points applicable to your situation and write a short and clear resume introduction (summary or objective), customized for the exact job position. The resulting text should tell a convincing story about you as an excellent fit for the company and capture the attention of the employer.
How to expose your work experience in a favorable light
Include essential details the Work Experience section
This section is usually the one that interests recruiters most of all, so it should be the main focus of your data scientist resume. List your jobs (or only the most recent ones, if you have a long work experience) in reverse-chronological order. If you lack real experience, consider including data science internships. For each job, provide the following information:
- Dates of employment (month and year for start and finish, or Present)
- Your job title
- Company name
- Your achievements on that job
Where possible, try to avoid any big gaps (more than six months) in your resume, especially in recent years. Even if your previous jobs are not from the field of data science it is best to include them, but there’s no need to get into too much detail. In the case when all your previous experience is from a completely different sphere, try to figure out what skills you actually practiced there that can be applicable to data science, and what value you brought to the business. If you are a fresh graduate without any work experience or internship, then skip this section.
Let's however return to an "ideal" case when you have some experience in data science/data analytics. For relevant past roles, use bullet points to concisely describe your data-driven accomplishments and the value you provided for the business at each position. The first bullet point should be the most impactful to convince a recruiter to read further. Avoid the mistake that many people make; listing their duties and technical aspects rather than highlighting their best business-oriented achievements in their previous jobs.
Use efficient words
It is important to be as specific, yet brief, as possible in your job descriptions. The ideal format for each bullet point is:
Action verb – Task – Outcome
Action verbs are meaningful and purposeful ATS-friendly verbs (those that an applicant tracking software of the company is more likely to search for). Some popular examples are:
Accelerate, Activate, Aggregate, Analyze, Assess, Augment, Automate, Build, Calculate, Calibrate, Coach, Code, Collect, Compile, Compute, Conceptualize, Conduct, Consolidate, Construct, Coordinate, Create, Debug, Decrease, Deploy, Derive, Design, Determine, Develop, Enable, Engineer, Enhance, Establish, Estimate, Evaluate, Execute, Extract, Fix, Forecast, Formulate, Identify, Implement, Improve, Incorporate, Increase, Initiate, Integrate, Interpolate, Launch, Lead, Lift, Liquidate, Manage, Mechanize, Mentor, Model, Operate, Optimize, Organize, Perform, Predict, Prepare, Propose, Recommend, Reduce, Refine, Regulate, Rehabilitate, Research, Solve, Streamline, Summarize, Supervise, Synthesize, Systemize, Troubleshoot, Update, Upgrade
Such verbs, apart from making your resume ATS-friendly, express the result of your professional activity much more precisely than generic verbs like do, collaborate, make, or work. However, don't confuse action verbs with inflated ones (e.g., use help instead of facilitate).
In addition to the "right" verbs, don't forget to include the keywords from the job posting and from the website of the company you are applying for. Remember, each resume should be customized to a particular role and company to increase your chances of success.
List your data-driven accomplishments. Try to provide hard numbers and concrete metrics of your positive impact on the business instead of using vague superlatives or generic adjectives (highest, strong, considerable, significant, etc.). Numbers look much more convincing when it comes to demonstrating to a potential employer that you understand the bigger picture and know how to render your technical skills useful for real business tasks.
Notice that we are not talking here about the model performance metrics but about the practical, measurable value the model you built brought to the company. For example, instead of stating that you created a machine learning model with 99% accuracy that increased client engagement rate by 21%, skip the information about the model accuracy and write the following:
Created a machine learning model that increased client engagement rate by 21%.
The numbers you mention can be about percentages, dollars, numbers of people you managed or coached, hours, or other time periods. It is fine to use rough estimates here.
Demonstrate your ability to collaborate
Data science is not all about numbers and modeling, but also about the ability to communicate your insights to your team and shareholders in order to help the company make strategic data-driven decisions.
Instead of writing in your resume that you are a good team worker with proven communication skills, you can demonstrate those skills in action by describing the jobs where you collaborated in a multidisciplinary and (probably) multinational environment with your colleagues, other departments including non-technical ones, and data consumers. Focus on the part you took in these projects and the measurable contribution you provided.
How to better showcase your projects
Include projects based on your experience and specialisms
This section is critical for junior and entry-level data scientists who often have limited to no work experience. The balance between the Work Experience and Projects sections is clear: the more work experience you have, the less space your Projects section should take up on your resume, up to totally excluding it in the case you are a senior data professional.
Whether you learned data science at university, on a master's program, or in a bootcamp, you most probably already have one or more projects on data science or data analytics. You can include data-related courseworks, guided and capstone projects from bootcamp, freelancing works, contributions to GitHub open source projects, and individual projects that you completed on a topic of your choice. If you don't have any projects to include, consider doing a mock one. After all, being a data scientist implies being curious about data and the insights that can be extracted from it.
If you have a lot of data science projects, you need to prioritize and select the most relevant ones and those you are most proud of. It is always better to showcase 3-4 good projects than a dozen mediocre ones. For an entry-level data scientist, it is fine and expected to have many disparate class or bootcamp projects on different concepts, with a variety of tools and techniques used. When you feel that you are ready to consider a specific business niche, start focusing on it, gaining domain knowledge, and creating projects related to that particular area.
Include key information for each project
When you have selected the relevant projects that you want to put in your data scientist resume, sort them in reverse-chronological order (or from the most relevant to the least relevant) and consider including the following information about each of them:
- The project name and the link to it in your Github portfolio
A clearly stated and concise project goal Then, using bullet points:
A brief and specific description of data sources, technologies, programming languages, libraries, tools, and skills used (avoid overusing technical jargon)
- Your individual contribution to the project (if it was a group project)
- The quantitative results of your work, demonstrating your ability to apply your technical skills to solve real-world problems.
Of course, many of the tips we discussed for the Work Experience section apply here also: focus on your meaningful accomplishments, use action verbs and the keywords from the job description/company website, avoid inflated and generic words, and use numbers and concrete metrics.
How to strategically highlight your skills
List skills by your compotence
This section is a must in a data scientist resume, regardless of seniority level. The best way to create a list of your skills for a particular job position includes the following four steps:
- Write down all the technical skills, languages, and tools explicitly or implicitly mentioned in the job description. Use the following list as an inspiration: A/B tests, Big data, C, C++, Data analysis, Data cleaning, Data mining, Data modeling, Data visualization, Data wrangling, Debugging, Deep learning, Hadoop, Hypothesis testing, Java, Keras, Machine learning, Mathematics, Matplotlib, NLP, NoSQL, Numpy, Pandas, PowerBI, Predictive modeling, Probability, Python, Quantitative analysis, R, SAS, Scala, Scikit-learn, Seaborn, Spark, SQL, Statistical analysis, Statistics, Tableau, TensorFlow, Unstructured data, Web scraping
- In the list you’ve created, mark all the skills that you really have and would be comfortable demonstrating in the interview. The best way to do so is to use your master resume as a reference. While you should not make up any skills that you don't actually have, think about adjusting your existing skills to specifically fit the requirements of a particular job position. For example, if you know several flavors of SQL including MySQL, and the job posting states MySQL as a desired skill to have, write only MySQL rather than mentioning the generic SQL, or listing all the SQL flavors you know. On the other hand, if the role of interest requires the knowledge of SQL in general, then write just SQL, without listing all the variants you know.
- Your list of skills should contain 6-10 items. The higher your level of expertise, the fewer items you can have on this list since your work experience will speak for you. If you are an entry-level or junior specialist, it is ok if you haven't had an occasion yet to apply all your toolkit to solve real-world tasks, but you still want to showcase to recruiters that you have these skills. Hence, if after the second step you have less than 10 skills in your list, consider adding some more skills that you actually have and that you think could be useful for the role, even if they were not mentioned in the job advert.
- Sort the skills in your final list, putting your strongest and most relevant ones first.
Don't include your skill level
While some resume templates have an option to rank each of your skills based on your familiarity with them, it is better to skip this step. Such evaluation can be extremely subjective and influenced by the Dunning–Kruger effect: your "proficient" can be "basic" for someone else and vice versa. To avoid selling yourself short or overselling yourself, don't rank your skills and manually remove the corresponding option from the template if there is one.
Don't include your soft skills
It is common sense that usually recruiters are looking for data professionals with certain soft skills such as teamwork, communication, and leadership. So, should you list these skills directly in the Skills section? Probably not. It would be much more impressive to demonstrate your soft skills in action in the Work Experience and Projects sections, combined with your technical skills to obtain valuable practical results.
Do you consider yourself a perfect team worker? Write about your contribution to collaborative projects. Are you a good leader? Describe your experience in managing a team or mentoring/coaching junior specialists. Finally, the best way to highlight your excellent communication skills is to concisely and impactfully present your experience and accomplishments in your data scientist resume.
How to better present your education
Typically, if you have any data-related work experience, internships, or projects, you should put Education after those sections (and also after Skills). Indeed, the higher your seniority level in data science is, the shorter the Education section should be. However, if you are applying for a highly academic position or if you are a fresh graduate with no experience, it is logical that this section will go first, right after the resume objective. For your education (or each education, in the case you have several degrees), include the following information:
- The highest degree type (B.S., M.S., Ph.D.) and major (even if it is not relevant to data science)
- University name
- Period of study (month and year for start and finish, otherwise use Expected graduation date)
The rest of the bullet points you may need to use only if you want to expand more on your education, i.e., if you have limited to no relevant work experience/projects, or if you are applying for an academic position:
- GPA (only for recent graduates and only if it is higher than 3.5)
- Academic projects (coursework, thesis, dissertation, etc.). Use bullet points if you have more than one academic project to showcase. Add a brief project description or list the covered topics.
- Academic courses: add the 2-3 courses that you consider the most relevant, optionally with grades.
- Academic achievements and honors
Don't mention in the Education section various data science bootcamps, skill paths, or courses you attended. You will add them later in Certifications.
Other helpful information to provide in additional sections
Let's see what other sections can be of use in your data scientist resume and help you show your passion and dedication to data science. Consider adding any of these sections if you have at least two bullet points for each heading, otherwise combine several categories together in a section called Extras.
Here you can put relevant courses and bootcamps if you are looking for an entry-level position. Apart from data science and data analysis certifications, think about including the courses in the subjects such as programming, linear algebra, probability, or statistics. If you have an official data science certification (Microsoft, IBM, SAS, Google), mention it in this section and also consider adding it to the headline of your resume, next to your title (e.g., Data Scientist, IBM Certified).
This section is valuable not only in academic but also in business contexts since it is excellent proof of your curiosity about the data outside of your work duties, your ability to work independently or with a team (in the case of a group publication) and clearly explain complex data-related concepts to a wide audience. For each article, include its name, the journal or magazine where it was published, the link to the online publication or your own blog (if applicable), and a brief abstract (or covered topics, or just keywords).
List only data-related conferences where you presented. Include the name of each conference, its geographic location, dates, the title of your work, and the name of your co-workers (if any).
Data science hackathons are a great way to show off your teamwork and technical skills, creativity, innovative mindset, and ability to produce real practical outcomes. Briefly outline the scope of your hackathon project, the product and its applications, your individual contribution, and achievements.
Put here any awards for your work or competitions you’ve won in a data-related sphere. These can be the awards for hackathons, Kaggle competitions, academic work, and publications. Add a short description to each award. Alternatively, you can consider adding this information in the corresponding sections.
Briefly describe the data science competitions you participated in and the results you achieved. These can be the competitions from Kaggle, DrivenData, DataHack, etc. You can find interesting ideas on this GeeksForGeeks post.
If you have experience of volunteering/moderating in some data science community or a good track record of problem-solving on Stack Overflow, use this section to give details and list your achievements. As usual, try to use numbers to back up your words (e.g, the number of technical issues resolved, questions with accepted answers, ratings, reputation, badges, read time, etc.)
It is very unlikely that you will need this section in your data scientist resume. Even if English is not your native language, your level will be quite evident from your resume itself and from the additional resources you would provide (GitHub, LinkedIn, etc.). Consider adding this section only if you are applying for a job abroad where speaking a second language would be an advantage and, of course, if you effectively speak that language at a proficient level. Here it is acceptable to add a supposed level of proficiency (e.g., native, fluent, advanced, upper-intermediate, intermediate).
Don't include interests/hobbies
Although some resume templates provide an additional section for interests/hobbies, it is better to skip it in your data scientist resume. While this information can be fascinating and beneficially characterize you as a curious person with many interests, it is not something that recruiters are searching for at this stage.
The same can be said about other information such as your traveling, participation in meetup events, social games, volunteering activities outside the data science field, proficiency in other languages (unless they are explicitly required for the current role), and your driver’s license. It is better to use this space in your data scientist resume for something else. You will have a chance to mention your hobbies and any other interesting information about yourself in the interview.
Data Scientist resume examples
Let's now take a more granular look at some mock data scientist resumes of different levels of experience to see how to put everything we have discussed so far into practice. Moreover, let's make it more fun (and also more efficient) and trace the professional evolution of the same (imaginary) person.
Junior data scientist resume
The example resume below is related to the stage when our imaginary candidate didn't have any experience other than a data scientist internship. This resume, as well as the subsequent ones, was created using the selection of free resume templates from Google Docs, with a few modifications.
- The resume is one-page long and contains a lot of information without looking overwhelming. There is enough white space on the page.
- The predefined template is not just black-and-white but has one more color, which makes it aesthetically more pleasant and easy to follow, but at the same time, not distracting.
- The overall layout is two-column, which is a good choice for a junior data scientist who wants to make up for the lack of real experience with a variety of other data-related activities and accomplishments, which means adding many sections to the resume.
- In the header, we see a professional-looking email and the clickable icons linked to the candidate's profiles on LinkedIn, Github, and Medium. (Side note: unfortunately, they are not clickable on the PNG picture above, only on the original Word and PDF versions of this mock resume. To this point, the clickability of the links in the final version of your resume is another important point to check.)
- All the subheadings are clear and laconic.
- Since the candidate has at least an internship experience to showcase, it was a good idea to include Summary rather than Objective.
- The summary is 3 sentences long, and concise but very informative. The candidate mentioned their internship experience, education, and data science training since he/she is still quite fresh in this field and also doesn't have a formal degree in data science. Then, they briefly described their skills (including soft ones) and, more importantly, achievements, demonstrated enthusiasm for the job position, and the potential business value they are able to bring to the company.
- In the Experience section, the candidate focused on their business achievements in the internship position, supported by concrete numbers. They started each bullet point with an action verb. No technical jargon, vague or redundant words are used here.
- Since the experience of the candidate is still rather limited, they listed their most relevant bootcamp projects, including the goal, skills and tools applied, and the link to each project in their portfolio.
- Because of the lack of experience, the candidate expanded a bit more on their education (adding the topic of their coursework, GPA, and relevant courses), volunteering data-related activities (giving some hard numbers also here), and data science training.
- In the Skills section, the candidate listed only their technical skills. The list is long enough and includes both the skills the candidate has and tools they can use.
- In the Publications section, the candidate included both their articles on data science and the one related to their original degree – Mathematical Finance.
Data Scientist resume
Now, let's assume that our candidate secured a job as a data scientist in a company called Silver Dollar (a fake one). After 2 years of working there, he/she is again on the search for a new professional challenge. This candidate is not so junior anymore, so their resume has changed accordingly:
- The focus in this updated resume has clearly shifted to the work experience rather than various "extras".
- The resume is one-page long, contains the most relevant information in a concise form, and there is enough white space on the page.
- The template has green color in addition to black and white, which makes it aesthetically more pleasant, easy to follow, and not distracting.
- The overall layout is one-column. This is ok in this case since the candidate included fewer additional sections than before and made them shorter.
- In the header, we see the same clickable icons linked to the candidate’s various profiles (most probably updated).
- The Summary section is 3 sentences long, but it is shorter than that of the previous resume. The candidate doesn't mention their data science bootcamp anymore since their experience speaks better for them. Also, the "auto-advertising" soft skills were removed. The internship experience is not emphasized separately as before but added to the 2 years of more recent work experience. What's more, we can clearly see that the candidate listed the skills different from those in the older resume (and the same can be said about the Skills section, which has become shorter). It doesn't mean that the candidate "forgot" their previous skills. Instead, most probably, they adapted the key points and achievements in Summary and Skills to suit the exact job description, which is always a great approach when writing a resume summary/objective.
- In the Experience section, the candidate described their main business achievements in both job positions and added meaningful metrics. Again, each bullet point starts with an action verb, no vague or redundant words, and shows only moderate use of technical jargon. Pay attention that the candidate uses a variety of action verbs for the bullet points (rather than, say, always using create or built) and also diverse measured metrics ($, %, hours, number of items).
- The Projects and Education sections are shorter in this updated resume, and Certifications and Volunteering are omitted completely.
Senior Data Scientist resume
After the last version of the resume, our data scientist changed jobs twice more and significantly grew professionally. Being again in search of a new job, they updated their resume for a potentially interesting position. So, how does their resume look now?
Let's highlight the most important changes:
- Apart from the header, the resume now includes only Skills, Experience, and Education, with Experience taking up the biggest portion of the page. There’s no summary, no projects, and no extra sections. The real work experience of this candidate speaks for them.
- The resume is almost black-and-white, with very limited use of orange color.
- The Skills section is significantly reduced, listing only the most global skills and tools (and no, many of them were not mentioned in the previous versions of the resume).
- The internship experience is dropped.
- The candidate's most recent position as a senior data scientist is more about strategies and management rather than data analysis and modeling.
To sum up, we came a long way exploring various approaches, tricks, practical examples, and templates to make your data scientist resume shine.
In some way, your data scientist resume is similar to a movie trailer. Just like the trailer is supposed to immediately capture the viewer's attention and hence convince them to watch the entire film, so does your resume, which is supposed to inspire a hiring manager to read it attentively, and invite you to interview.
Of course, writing a perfect resume is only the very first step in your application process, which doesn't automatically guarantee you success in the interview itself. However, without passing through the inevitable gates of ATS and then a hiring manager/recruiter, you will not be able to proceed any further. Hence, it is vitally important to dedicate enough time and make due efforts to build an efficient and convincing data scientist resume, tailored to each individual role you apply for.
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