Data Science Resume Examples (Apple, Microsoft, Adobe, etc.)

As you can imagine, data science jobs at top companies are highly competitive. Most candidates don’t make it past resume screening, let alone receive a job offer. 

To increase your chances of getting to the interview stage, use this step-by-step guide on how to write a top data science resume or CV.

In this article, we offer tips and expert insights from data science recruiters, including 3 real examples of data science resumes that earned candidates offers at companies like Apple and Microsoft.

Here’s an overview of what we’ll cover:

1. Data science skills for your resume
2. 7 golden rules for data science resumes
3. 3 examples of data science resumes
4. Data science resume template (downloadable)
5. How to write a data science resume that gets you into top companies (section-by-section)
6. Your data science resume checklist

Get expert feedback on your resume from data science ex-interviewers

1. Data science skills for your resume ↑

The role of a data scientist can vary widely depending on the company and industry, but there is a set of core skills that recruiters expect to see on your resume if you want to stand out.

Depending on what type of data scientist you want your resume to portray you as, certain skill areas will be more important to include than others. It will also depend on the company you’re targeting. 

Some companies lean more heavily toward technical skills, such as machine learning expertise (e.g., tech firms like Google, Meta, and other FAANG companies), while others emphasize business acumen and communication (e.g., consulting firms and consumer brands). 

Before you apply, review the job description carefully and make sure you’re tailoring your resume to highlight the skills they’re looking for in a candidate. 

Is the company looking for an applied data scientist, a machine learning data scientist, or another specialty? Is it a managerial position where you’ll need to showcase leadership qualities on top of technical finesse? In addition to the skills we'll mention below, you'll want to include ones relevant to the position itself.

Much like engineers and software developers, data scientists are often evaluated on a blend of technical and soft skills. Below, we’ll walk through some of the most essential competencies to showcase in your resume.

1.1 Hard skills (technical)

Hiring managers want to see that you have the technical expertise to analyze data, build models, and generate insights that drive decisions. Depending on the company, the emphasis may fall on domains like statistical modeling, machine learning, or data engineering.

Here are some of the key technical skills you may want to highlight:

• Programming skills in languages such as Python, R, and SQL. Recruiters want to know that you can efficiently extract, manipulate, and analyze large datasets. Be sure to list specific libraries and frameworks you’ve used (e.g., pandas, scikit-learn, TensorFlow).
• Statistical and mathematical skills to design experiments, evaluate hypotheses, and apply the right analytical methods. This could include regression, classification, time-series analysis, and A/B testing.
• Machine learning skills for roles that emphasize model building. Employers look for experience in training, evaluating, and deploying models at scale. If you’ve worked on production-grade ML pipelines, make that prominent.
• Data visualization and storytelling skills using tools like Tableau, Microsoft Power BI, Matplotlib, or ggplot. Recruiters will want to see that you can translate raw data into insights stakeholders can understand and act upon.
• Data wrangling and big data skills, such as working with messy, unstructured data and using tools like Apache Spark or Hadoop for large-scale processing. Highlight projects where you’ve dealt with real-world, imperfect datasets.

1.2 Soft skills

Beyond the technical toolkit, successful data scientists need strong soft skills to ensure their work has an impact. This is especially true if you’re applying for a consulting or consumer-focused company.

Here are some soft skills you may want to highlight in your resume:

• Business acumen to connect data analysis with business goals. It’s not enough to build a model. You need to show that you understand the “why” behind analysis and prioritize what will create value.
• Problem-solving skills to structure ambiguous problems, identify relevant data sources, and choose the right approach under uncertainty.
• Communication skills to explain complex technical findings to non-technical stakeholders. This means not just building dashboards, but also telling a compelling story around the data.
• Collaboration skills to work effectively with cross-functional teams such as engineering, product, and marketing. Recruiters want to see that you can bridge the gap between technical analysis and business decision-making.
• Adaptability and curiosity, since the field of data science is constantly evolving. Show that you’re proactive about learning new technologies and techniques, and comfortable experimenting with new approaches.

Demonstrating these skills in your resume (ideally through concrete projects, quantified results, and specific technologies you’ve used) will signal to recruiters that you’re well-equipped for the role.

2. Seven golden rules for data science resumes ↑

We asked James (data analyst) Candace (career and resume expert) what advice they’d give to someone writing a resume to get into top companies, specifically in tech. She has helped a lot of people get into FAANG and evaluates tech resumes every day, so she knows what she’s talking about. 

However, the advice below can still apply if you’re applying as a data scientist to a consumer-focused company or consulting firm. 

Here are some important tips to keep in mind as you craft your resume:

Tip #1: Answer recruiter questions immediately

Recruiters want to see if you meet job requirements within the first few seconds. There’s one thing that all recruiters want to know immediately: years of role-relevant experience. 

So, if you're applying for a data science role, they'll want to see how many years of data science experience you already have. 

Make it easy for them by presenting your key information clearly. Recruiters are extremely busy and will be rifling through dozens of resumes besides yours. Make sure they don’t need to dig to find what they’re looking for, or else you may risk being overlooked.

To do this, you can include bullet points with the key information at the top of your resume. This ensures it’s the first thing recruiters will see. 

Tip #2: Avoid using unnecessary design features

For data science roles, there is no upside to using a fancy resume design. It won't impress recruiters and, at worst, it could actually prevent your resume from being properly processed.

"Design features like pictures, columns, photos, etc. can prevent ATS systems from correctly scanning your resume," says Candace.

You should also avoid including your portrait in your resume because this goes against employment and discrimination laws in most countries. It’s also another potential problem for ATS systems.

Tip #3 Be explicit about the locations you’re open to working at 

So many people fail to do this, but if tech recruiters are going to approach you for roles, rather than the other way around, they'll need to know the locations you're available to work at.

If you're willing to relocate for the right role, make that clear in your resume. So, instead of putting "San Francisco" under your name next to your email, you might put something like: "Locations: San Francisco | Remote | Hybrid within a 30-mile radius of Bay Area.”

Tip #4: Cut out all the waffle

Recruiters see many resumes with sections for ‘personal statement’ or 'objective' at the top, which take up valuable space without saying much at all.

Resumes should be specific. Personal descriptions like “Experienced data scientist passionate about…” are subjective and don’t tell your recruiter anything. 

Instead, be specific. List your specialized skills and quantifiable accomplishments, such as how many years of experience you have or projects you’ve successfully executed in the past.

Tip #5: Start with strong verbs

To make those bullet points even stronger, "always start with strong action verbs: analyzed, modeled, executed, led, etc.,” says James (data analyst). 

Verbs communicate the action you took to achieve your numbers and highlight the exact role you played in a project. 

Tip #6: Tell a better story with numbers

This is worth repeating again and again: quantify your achievements. The most effective resumes are packed full of metrics that put achievements in context.

In data science, results speak louder than responsibilities, so let the numbers do the talking.

Be clear, concise, and ROI-focused in every bullet. Each bullet should show action, skill, and results in one punchy line. 

So, if you’ve gotten great results in a past project, share your KPI metrics. Without numbers, your achievements are hard to evaluate.

Tip #7: Use the skills section to include keywords for your role

You don't want to jam your resume full of keywords just for the sake of using them. But with ATS systems being commonly used in the recruitment process, your resume should mention the skills, tools, and technologies relevant to your target role.

A ‘Skills’ section can help recruiters quickly see if you fit their requirements. It’s also a great way to get keywords into your resume, especially if the job you’re applying for requires experience in specific technologies or proficiency in foreign languages.

3. Three examples of data science resumes that worked for Apple, Microsoft, Adobe, etc. ↑

Before we start guiding you on how to write your resume step-by-step, take a look at some real examples that got candidates interviews at top tech companies.

You'll notice they follow different formats, and none fully follow the guidelines we set out below in Section 4. We think this shows two things:

1. There are many acceptable ways to write a resume.
2. Your resume doesn't have to be perfect, as long as it demonstrates your skills and achievements effectively.

Let's take a look.

3.1 Data science resume example 1

Let’s call this candidate Idris, who has held roles at top tech companies. Though we had to blank them out, these are companies you’ve likely heard of, and you may even be using their digital products on a regular basis.

Here's our feedback on this resume:

• Experience: Though company details have been redacted, Idris has worked with some notable global brands. Despite having held only 3 corporate roles and 1 independent role as a data scientist consultant, he strengthens his resume by mentioning the projects he built and led in the past.
• Quantifying impact: Idris frames his impact with real numbers. He specifies details like percentage growth in key metrics, which immediately position him as a strong performer who is able to use data to bring results.
• Higher education: Aside from having graduated from top universities, Idris further highlights his advanced knowledge of data by specifying the relevant domains he studied. He mentions technical courses like applied statistics and machine learning, and balances it with business-related training in business development, leadership, design thinking, and strategy. This positions him as a well-rounded candidate who cares about both technical knowledge and business acumen.
• Key skills: At the bottom of his resume, Idris lists his additional skills in programming languages, data tools, and leadership.

3.2 Data science resume example 2 (Adobe, Microsoft, Amazon)

Let’s call this candidate Kevin. They’ve also held positions at top tech companies, such as Adobe, Microsoft, and Amazon.

Here's our feedback on this resume:

• Experience: We had to blank them out, but Kevin had worked for some notable tech companies in his country. He has also held senior positions. This makes him a great candidate for more senior roles.
• Quantifying impact: Like Idris, Kevin frames his impact with real numbers. He specifies details like completion rates, growth percentages in search usage, and increases in conversion rates. This shows recruiters that his work in data gives companies real value.
• Technical leadership skills: On top of specifying his numerical impact, Kevin supports his roles by specifying projects he created, developed, and led. This shows recruiters that he has experience driving large projects to completion and likely has experience leading data science teams.
• Tools: In every single role, Kevin specifies which tools he used during his time in that position. He includes programming languages and data technologies.
• Published work: While Kevin does not have a section dedicated to key skills, he does include a section for his published academic work. This shows intellectual rigor and a strong passion for research, along with a commitment to the field that extends beyond corporate experience.

3.3 Data science resume example 3 (Apple)

Let’s call this candidate Connie. Connie has previously held data scientist roles at top companies like Apple.

Here's our feedback on this resume:

• Experience: We had to blank them out, but Connie has worked for top tech companies, market research companies, and consumer-focused companies. She has also held managerial positions, which makes her a great candidate for senior roles.
• Strong leadership: Connie’s bullet points showcase the significance of her work by communicating her leadership with strong verbs, such as “innovated,” “directed,” “oversaw,” and “coached.” This shows her proven track record in positions of responsibility.
• Quantified work: Connie has worked on projects that span massive portfolios and budgets, some of which go into the millions and billions. This shows recruiters that she can be trusted with sensitive information and knows how to utilize resources for her projects.
• Descriptive summary: At the top of her resume, Connie provides a detailed snapshot of her work experience, specifically with supporting C-suite executives. This shows recruiters her readiness for high-level, impactful work and her ability to communicate with a company’s most important stakeholders.

4. Data science resume template ↑

Unlike the examples listed above, this is not a real resume. Instead, it's an amalgamation of the high-quality resumes that candidates have shared with us before going on to work at top companies.

It belongs to an imaginary mid-level data scientist named Anna Sales, but you can follow the overall structure no matter what your role is.

Click here to open this data science resume template as a PDF.

Click here to download this data science resume template as a Google Doc.

Now, let’s take the first step in building a data science resume that's good enough to get into a top company.

5. How to write a data science resume that gets you into a top company (section-by-section) ↑

Now that you’ve seen examples of what you should be aiming for, plus some key tips, let’s go through the resume-building process step by step. 

5.1 Study your target company and job description

Before you start writing or editing your data science resume, start by doing some research. This will help steer you in the right direction for what your resume should look like.

First, find a job specification for your target role and read it thoroughly. Use it to shape your resume in the following ways:

1. Identify the exact type of data science role you’re aiming for. Data science covers a range of positions and seniority levels, including data analyst, applied data scientist, machine learning data scientist, or research data scientist.
2. Figure out what type of profile the job description is looking for. Identify which skills are most crucial for the role. For data science positions, companies typically look for expertise in areas such as programming (Python, R, SQL), machine learning, statistical modeling, data visualization, data wrangling, and cloud-based tools. Depending on the company, business acumen and domain knowledge (e.g., e-commerce, healthcare, fintech) may also be important.
3. Prepare to adapt your resume accordingly. Look into the keywords of the job description and, as much as possible, use them where they’re applicable. Suppose your previous jobs or internships are not directly related to data science. In that case, you can phrase your descriptions to highlight relevant transferable skills, such as quantitative analysis, problem-solving, collaboration, technical expertise, or stakeholder communication. The same applies to extracurriculars or academic projects; frame them in terms of data collection, analysis, or insights generated.
4. Zoom in on a few of the responsibilities in the job description that you think are most important. Search for specific examples from your past that demonstrate your experience in doing the same thing, or something very similar. Find the numbers to back it up where possible, so you’re ready to include this information in the work experience section later on.
5. Take note of the language used in the job description. You can match specific verbs and phrases where you find them appropriate. 
6. Research the company. For example, if you’re targeting a data science role at Netflix, their culture memo highlights values like “Judgment,” where they specifically describe using “data to inform your intuition.” Show your judgment by highlighting projects where you built something new and quantify the resulting business outcomes.

Does this mean you’ll need a different iteration of your resume for every data science job you target? Ideally, yes. But there will be a lot of overlap, so you’ll typically only need one version that you can strategically edit for specific job applications later on.

We looked at some of the latest job postings at major companies like Google, Meta, JP Morgan, Capital One, and others, then aggregated the data to find the most common data science job requirements for such companies in recent years. Here’s what we found:

This list of requirements is a good base for your resume. You can later tweak the details based on the specific role you’re targeting. 

Once you’re done with your research, you can move on to writing your data science resume.

5.2 Choose a design

The design of your resume should have one objective: to convey the right information in a way that is clear, easy to digest, and professional. With our resume template as your base, you’ve already achieved that!

As we mentioned earlier, avoid using unnecessary design elements. Some recruiters are even put off by “creative” or unique resumes. It can also prevent your resume from being properly processed.

Most importantly, keep your resume as concise as possible. If you’re a fresh graduate with no experience, aim to present all necessary information on one page only. Recruiters won’t have time to leaf through multiple pages when they’re looking through dozens, if not hundreds, of resumes.

Keep it clean, concise, and simple. 

5.3 Choose your sections

There are lots of ways to write a resume, and the exact sections you include are up to you.

We recommend using the following sections for a data science resume because we know this approach works for top companies like FAANG, major consulting firms, and top consumer brands.

You may tweak the order. For example, if you’ve just graduated or only have a year of experience, you can start with your ‘Education’ section.

As for the layout of your resume, here are some best practices to keep in mind:

• Choose a simple, professional-looking font: We recommend using size 10-12, black font on a white background. Arial, Calibri, and other plain sans serif or serif fonts are fine.
• Use bullet points: This makes it easier for recruiters to pick up relevant details at a glance. You don’t want to overwhelm your resume with blocks of text.
• Double-check for 100% neat, consistent formatting: Keep bullet points and margins aligned. Apply font formatting (such as bolded letters) where it’s appropriate, but don’t overdo it. 
• Save it as a PDF to make your resume look uniform on any device.

5.4 Start writing

The good news is that you don’t have to get your resume perfect the first time. A strong resume is usually rewritten and tweaked multiple times. Here’s a guide on how to write up each section:

5.4.1 Personal details

The purpose is not the place to try and impress recruiters. Its only purpose is to list your key basic personal details: full name, contact information, location, and your LinkedIn profile if you have one.

Keep the following best practices in mind when writing this section:

Once you’ve listed your basic key details, you can also include a short personal bio that summarizes your experience in a few sentences. 

However, this portion is optional and not particularly important, especially if you have plenty of work experience and achievements to list in other sections. If you feel including a personal statement will just take up unnecessary space, skip this part.

5.4.2 Work experience

This is probably the most important part of your resume to get right, but the easiest to get wrong. Many candidates think their work experience speaks for itself and will simply list their roles, with a few main responsibilities.

However, we recommend a much more powerful approach.

The work experience section should include previous work positions you’ve held, as well as your main achievements in these roles. Here are some more important points to help you put this section together.

Instead of simply listing your responsibilities per role, highlight your actions. This means starting each bullet point with an action verb. These verbs should relate to the key skills from Section 1 that top companies look for in data science resumes (programming skills, data visualization skills, etc). Each bullet should begin with relevant and powerful verbs, such as “Executed,” “Structured,” “Developed,” or “Conducted.”

Here are some examples of strong wording in bullet points:

• “Developed an algorithm that increased search usage by 33%”
• “Built an ML model that accurately predicts the likelihood of damaged return order packaging. This model saves the company approximately $1.5M per year in seller claims.”

Choosing actions that are relevant to the essential data science skills will also mean that your resume contains the keywords that recruiters (and sometimes ATS) will be looking for.

Whenever possible, include numbers to show the scale and results of your work.

Quantifying results (like deal size, revenue growth, cost savings, or efficiency gains) makes your contributions more tangible.

For example, you might write something like: 

• “Built a customer churn prediction model with 85% accuracy, helping reduce churn by 12% year-over-year”
• “Developed a recommendation engine that increased average order value by 8% across 500K+ customers”
• “Automated data pipeline processing for 50M+ records daily, cutting reporting time by 40%”
• “Designed and deployed an A/B testing framework that improved experiment turnaround time by 35%”
• “Created forecasting models that reduced stockouts by 18% across 200+ retail locations”

If your target role is multi-faceted, such as a managerial position, balance may also be an important consideration. If so, try to demonstrate a range of skills in this section. For instance, some bullet points can focus on leadership skills, and others focus on technical problem-solving results.

Finally, don’t be shy or humble in your ‘Work experience’ section. Communicate your impact and be specific to make each bullet point count.

Quick checklist:

By following this structured, metrics-driven format, your ‘Work experience’ section will communicate both your technical abilities and real-world impact. Both are important to show recruiters that you have exactly what top companies are looking for. 

5.4.3 Education

This section should be extremely concise and clear. Hopefully, your educational achievements can do the talking for you. All you can do here is present the necessary information with the right level of detail.

Follow the tips below to make sure you get it just right.

Note that if you’re a recent graduate with internship experiences instead of relevant work experience, this section should follow the ‘Personal information’ section, and you may want to go into more detail. Otherwise, you can place this section after the ‘Work experience’ section.

5.4.4 Awards and publications

The more experience you have, the easier it should be for you to find 2-3 strong bullet points that demonstrate awards, published research work, and other notable achievements. You may also include extra-curricular leadership positions in this section.

If you haven't won any awards, published any work, or can't think of any strong leadership examples outside your day-to-day role, then consider omitting this section entirely.

5.4.5 Skills 

Your data science resume needs to show that you're adept at using a range of relevant tools, methodologies, and techniques. Listing them here makes it easier for a recruiter to quickly check if you meet their requirements.

If you're applying for a highly technical data science role, stick to skills that are directly relevant to the role. This can include data science software proficiencies and certifications.

You can also include skills that signify soft skills and add personality to your resume. This can be your foreign language proficiencies, entrepreneurial projects, etc. 

If you need to save vertical space, list your skills in sentence form and separate them by commas, rather than individual bullet points.

Lastly, don’t include generic or irrelevant interests that everyone likes doing, like “watching Netflix” or “spending quality time with friends.” 

5.5 Proofreading and feedback

Don’t skip this step! Use a grammar-checking tool and proofread until it’s perfect. This is harder than it sounds because multiple revisions after the initial proofread can easily create new, hard-to-spot errors. The only solution is to proofread again after each tweak.

We recommend saving your resume as a PDF file (unless the job description says otherwise) and checking that it opens properly with the correct formatting on a Mac and PC.

Receiving feedback is also important. Share it with a trusted friend or partner, and they’re more likely to see mistakes that you haven’t noticed. Of course, if you can share it with an experienced data science recruiter / interviewer, that can give you a big advantage over other applicants.

6. Your data science resume checklist ↑

Almost ready to send your data science resume? Use this checklist to make sure you’re following all the best practices we’ve recommended above.

If you can answer “Yes” to every question, then you’re ready to hit "Apply.”

If you’ve used all the tips in this article, then your resume should be in good condition and give you a fighting chance of getting a job at a top company.

Looking for more guides related to data science? Here's a list of valuable references 

• Data science case interviews 
• Data science interview prep
• TikTok Data Scientist Interview
• Google Data Scientist Interview
• Meta Data Scientist Interview
• Amazon Data Scientist Interview
• Best Mock Interview Sites (for Engineers, PMs, Data Scientists, etc.)

7. Is your resume good enough for a top company? ↑

If you're going for one of the top data science jobs, having a resume that's "fine" may not be enough. Getting your resume from "fine" to "outstanding" usually requires feedback from someone who knows the industry, such as an ex-recruiter or manager with experience hiring within your target company.