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Our Step-by-Step Guide to Landing a Data Science Job

If you’re tech- or quant-minded, you’ve probably heard about the booming data science job market. As companies in almost every industry continue leveraging data-driven decision making, data scientists will stay in high demand, with the US Bureau of Labor Statistics forecasting 21% headcount growth in the field by 2031, over four times the growth it projects for all occupations.

Given this data, it’s easy to think that data science jobs are simply there for the taking, but the reality is more complicated. Skilled data scientists, after all, can demand handsome salaries: $138,780 on average in the US, more than twice the median household income of $67,521.

With so much money up for grabs, competition for data science jobs is fierce: nobody’s just waltzing into one of these positions. So how do you land a data scientist role these days? In this article, we’ll dive in with a step-by-step guide that you can follow to get started down your data science career path. Even better, this guide will work for the aspiring data analyst, data engineer, business analyst, data architect, or any of a number of data-centered jobs.

What is data science?

Before diving in, let's review the fundamental concept of data science. According to IBM, data science involves applying a multidisciplinary approach to extract valuable insights from large and constantly increasing volumes of data generated by modern organizations. This includes organizing and processing data for analysis, conducting advanced data analysis using machine learning algorithms and models, and presenting results through data visualization to reveal patterns and help stakeholders make informed decisions.

In the role of a data scientist, you will follow the data pipeline, which involves:

  • Defining research interests and questions,

  • Structuring unstructured data (or "raw data") for use,

  • Analyzing the data,

  • Communicating findings effectively through data visualization tools.

There are also several other positions within or related to data science that focus on using data to achieve business objectives, such as:

Data Analyst

Data analyst is an extremely broad title — so the day-to-day of two data analysts at different companies might look very different. Typically, however, a data analyst gathers data and readies it for analysis, and then analyzes it to yield business insights that the analyst will then communicate to relevant stakeholders, often through data visualizations. Though there is some overlap between the responsibilities of a data scientist and a data analyst, data scientists are usually more concerned with creating new techniques, tools, and processes for data analysis, while data analysts more often use existing techniques, tools, and processes. Check out our article which dives deeper on the topic of becoming a data analyst.

According to, in the US the average data analyst salary is $81,719.

Business Intelligence Analyst

There’s certainly lots of overlap between a data analyst and a business intelligence analyst. Indeed, sometimes it just comes down to what title a company gives a position. But, in theory at least, what sets a BI analyst apart? According to Indeed, a business intelligence analyst — or, sometimes just “business analyst” — is more narrowly focused than a data analyst on the metrics, sometimes called “key performance indicators” (KPIs), that can be used to evaluate a company’s performance. By definition, business intelligence focuses on the past, while the umbrella field of data analytics can include areas like prescriptive and predictive analytics  that focus on future activity.

According to, in the US the average business intelligence analyst salary is $84,803.

Data Architect

A data architect designs efficient pathways, protocols, and systems for data collection, preparation, and storage according to a business’ specific needs, capacity and resources.

According to, in the US the average data architect salary is $124,771.

Data Engineer

Data engineers and data architects are commonly confused, but the distinction is actually quite simple. While a data architect designs efficient pathways, protocols, and systems — plans them out — a data engineer focuses on creating them. 

According to, in the US the average data engineer salary is $112,555.

How to get a job in data science

Now that we’ve reviewed what exactly data science is and the different careers included in and adjacent to data science, let’s get down to how you can start yourself down a data science career path.

Step One: Get educated.

The first, and most important, step towards landing a data science job is learning core data science skills, both the technical skills that will help you deal with the data and the soft skills that will help you derive and communicate insights from it.

Data Science Skills

Data science skills include the following:

  • Applied mathematics: Probability, statistics, and linear algebra are crucial both for statistical analyses and for developing machine learning models.

  • Programming: Programming skills, including fluency in programming languages like SQL, R, and Python, are essential to be able to access data and perform advanced analytics.

  • Statistical analysis: Skill performing statistical analyses such as linear regression is required to turn raw data into actionable insights.

  • Machine learning: The ability to write machine learning (and especially deep learning) algorithms and models allows data scientists to more efficiently and effectively separate signal from noise, especially when dealing with big data sets.

  • Data management: Before they can even begin analysis, data scientists need to employ skills in data collection (including data mining), data cleansing, and database management.

  • Data visualization: A core component of communicating new business insights derived from data is data visualization, usually using software like Tableau.

  • Soft skills: What sets the great data scientists apart from the good ones, especially later on in their careers, are core soft skills like communication, leadership, and critical thinking.

If you’re an aspiring data scientist looking to start building out your skill sets, there’s an abundance of options depending on your particular background.

Data Science Bachelor’s Degree Programs


  • Aspiring data scientists willing to spend the time and money on a four-year degree


  • A data science bachelor’s curriculum will cover all the essential skills we noted above, though for more advanced areas like deep learning the instruction will be fairly minimal.


  • Data science bachelor’s degrees offer the surest path to a data science job for those without existing experience.

How much?

  • Tuition for bachelor’s degree programs can vary widely, especially between in-state tuition at public universities and tuition at elite private universities. 

  • In general, undergraduates can expect to spend between $10,000 and $60,000 on tuition per year before living expenses.

What next?

Data Science Bootcamps


  • Those looking to get into data science who 1) don’t want to pursue traditional degrees and 2) want to study online


  • Students can expect to cover much of the same material in a data science bootcamp as someone in a bachelor’s program, though in much less time and thus in much less detail. 

  • Fundamental skills in mathematics are generally taught through self-directed preliminary courses required to be completed before beginning the bootcamp.

  • Bootcamps will also usually offer 1:1 mentoring or other career services and emphasize independent projects that students can use to start their data science portfolios. 


  • Because they are online, bootcamps are shorter, cheaper, and more flexible than traditional degrees, while still providing a feasible path to a data science career.

How much?

  • Bootcamp tuition varies drastically depending on what organization is offering the program. It’s not uncommon for three-month, full-time programs to cost upwards of $15,000, though part-time, more self-directed bootcamps can cost considerably less. 

  • Some bootcamps offer financing, installment plans, or income-share plans where you pay back a fraction of your income once you get a job. 

  • Other bootcamps have guarantees that you will get a job offer or you get your money back.

What next?

Data Science Certificates and Other Short Courses


  • Those looking to get into data science who don’t want to commit to a bootcamp or who already have some relevant skills.


  • Data science certificate and other short courses will either give a broad overview of data science without diving too much into the details or will focus only on one aspect or skill, such as coding with Python or data visualization.


  • Non-bootcamp certificate programs and other short courses allow for more targeted, flexible, or bespoke instruction than a bootcamp could offer. This is especially useful for someone who already has some relevant experience working with data.

  • Certificates can be displayed on a resume or LinkedIn to communicate your expertise to recruiters.

How much?

  • Generally these options are less expensive than bootcamps, with tuition ranging from free to a couple thousand dollars. 

  • Some online learning providers like DataCamp offer training for a flat monthly fee.

What next?

Data Science and Data Analytics Apprenticeships


  • Data science and data analytics apprenticeships are open to individuals from a variety of backgrounds looking to transition into data science, from those holding GEDs and associate’s degrees with little formal experience to those already working at a company in a different capacity.


  • Apprenticeships combine rigorous instruction in data science and data analytics basics with on-the-job training in which apprentices make real contributions at their companies.


  • Students seek out apprenticeships for a low-risk, high ROI opportunity to study data science and data analytics while gaining experience embedded on a team at a real company.

How much?

  • While the other educational opportunities listed here generally require the learner to pay for their education, apprenticeships are unique in offering training for free. Some even include a modest salary. After completing their program, an apprentice can go on to earn a full data science wage, either at the company where they apprenticed or elsewhere.

What next?

  • Check out our full run-down of data science and data analytics apprenticeships for more information on these exciting opportunities.

Data Science Master’s Degree Programs


  • Individuals who already hold bachelor’s degrees in STEM disciplines or who are able to show aptitude or existing ability in programming and applied mathematics. 


  • The curriculum of a data science master’s degree will resemble that of a data science bachelor’s degree to an extent, but generally courses will be more advanced, with fundamentals either not offered or required to be completed in advance of beginning the degree proper.


  • Master’s-level study provides more advanced data science training and can unlock higher salaries than either bachelor’s programs or bootcamps.

  • While universities frequently offer full-time in-person master’s programs, there are more and more part-time and online master's in data science program options for students looking for the flexibility to continue working or care for a loved one while they study — the best of both worlds.

How much?

  • Tuition for data science master’s programs can vary widely, not only because of the difference between public university in-state tuition and private university tuition, but also because of the availability of online and part-time programs. 

  • Graduate students can still expect to spend between $10,000 and $60,000 on tuition per year before living expenses.

What next?

Step Two: Get experienced.

While education can help you learn the skills needed to succeed in data science, most employers want job candidates to also have real-world experience, even for entry-level positions. There are several different ways you can begin building this experience.


If you choose to pursue a traditional bachelor’s or master’s degree, it’s a good idea to do one or more internships during your summers off. Many times, a degree program will have existing industry connections through which they can place their students in internships. You can also find internships posted on company career pages and on LinkedIn, Indeed, and other job boards.

Independent or Collaborative Projects

Even if you have an internship on your resume, it’s a good idea to supplement this experience with a portfolio of data science projects that show off your abilities and interests, often hosted on GitHub or a personal website. Most bachelor’s, master’s, and bootcamp programs will lead students through the process of designing and executing an independent project, and once you learn this process you can go off on your own — the sky’s really the limit. Increasingly, aspiring data scientists are teaming up with others from around the world on open-source projects like those offered by Spotify. Working on one of these projects offers not only great training, but an opportunity to network.

Freelancing and Pro Bono Work

Another way to build experience is through freelancing, though if you are brand new in data science it can be difficult to land your first gig. In this case, you might consider doing pro bono work through a social-good outfit like DataKind, Catchafire, or Statistics Without Borders. While you won’t get paid for this work, oftentimes you’ll be allowed to showcase any work product in your portfolio.

Practice Problems

If you want to practice your skills in a stress-free environment, there are loads of practice problems available for free online to keep you sharp. This is particularly the case for Python: resources like Practice Python, Real Python,  and PyNative can help you gain fluency and work towards acing interview coding tests.

Join a Data Science Club or Organization

Many universities and communities have data science clubs or organizations that offer opportunities to work on projects and participate in hackathons and other events. These organizations can provide a supportive learning environment and a chance to network with other data science enthusiasts. Notable examples are Women in Data Science (WiDS), a global organization that hosts conferences and events focused on the advancement of women in data science and related fields, and the Association of Data Scientists (ADaSci), a professional organization that brings together data scientists from various industries and provides networking opportunities, educational resources, and events to help members advance their careers.

Step Three: Get polished.

Once you have the skills and experience, it’s important to make sure you’re showcasing them in the best way possible. This will not only help you land a job, but will be good training for the kind of persuasive storytelling you’ll have to do as a data scientist. In addition to your portfolio, which we’ve already mentioned, you’ll want to make sure to have an up-to-date, polished resume and practice your interviewing skills.

Remember, when you apply for a job, you’re applying to play a specific role as part of a team, so for each job you apply for you’ll want to tweak your resume accordingly and practice answering why you want to work there and why you’d be a good fit.

For more, check out our data science resume and interview tips.

Step Four: Get busy.

So you have what you need to perform well on the job and you can demonstrate this to an interviewer — how do you land your first interview? While your first impulse might be to apply widely — and you should certainly apply to many different places — you should also leverage your professional network to develop some leads as you go about your job search. Oftentimes, a connection from a fellow alumnus or alumna or a family friend is all you need to make sure your resume gets read and you get an interview.

Once you land an interview, be ready for a protracted process: you will likely have to pass several different interview rounds before you get a job offer. Oftentimes, this will involve speaking to potential co-workers at varying levels of seniority, and you will likely also be asked to complete a take-home project so that they can assess your skills and fit for the role.

Step Five: Profit!

It’s a long road, but if you work diligently and practice patience, you will succeed in getting your first data science job offer, and maybe several! But getting your first job is just the beginning. To move up, data science professionals need to keep their skills sharp and maintain growth mindsets. When the time comes for you to do so, we’ll be here with the latest developments in the field and great options if you want to level up with another degree.