How to Get a Data Science Job: The Ultimate Guide
Here we provide a very specific and practical guide on how to get your dream job in the Data Science world.
The data needs of every industry have made Data Science one of the fastest growing fields out there, and any data science role has the potential to blossom into a high growth career. At the same time, Data Science is still a relatively new field, and the responsibilities, structure, and needs of any particular role can vary wildly across different industries and companies. There's been a lot of interest in landing a job in Data Science in recent years. So how do you do it?
How do You get a Data Science Job?
Breaking into Data Science
For those who are looking to start off their careers in Data Science, whether it be as someone just starting out in their career or as someone in the midst of a career change, it is important to think about what sort of baseline is needed for entry level data science roles. Do your due diligence in examining your own skills and experiences: what aspects do you need to build up on? Data science has a heavy emphasis on computation, math, statistics, and programming, and while not absolutely necessary, a college degree or advanced degree is extremely helpful for getting your foot into the door in data science.
Another important step is to reflect on where your personal interests lie, and how that may or may not diverge from your past experiences. For those with previous work experience in other fields, what skills could potentially carry over? There are broad data needs and data science work available in almost every industry. This means that even if you don't have an explicit past experience working with data: your professional knowledge can still carry over, especially if you're looking to stay in the same industry and are in a position to leverage that previous experience.
While data science in the tech and information industries are the most commonly known, there are still many data needs in the finance and health care sectors, both major industries in their own right. There's also an increasing need for data science work in education, hospitality, entertainment, and even in the public sector, meaning any past experience can be helpful as you look to break into Data Science. A past in medical work means you bring a unique perspective to any role with health care data. A history in marketing means you have knowledge of the business world, and may be uniquely suited for a business analyst position, or at least more so than, say, a generic data science major, despite them having a formal data science background.
Remember that Data Science is a rapidly expanding industry, and many companies are even looking internally to hire for their data roles, with the thought process that, for example, training business people in technical data analysis is easier than teaching data scientists abstract business concepts.
Check out our blog post on How to Start Learning Data Science from Scratch!
Going back to examining your skills and experiences, take a moment to understand where your own strengths lie. Data Science covers a broad set of skills, and it's important to categorize and work on any areas you may be lacking.
There are a variety of resources and educational platforms available online, including some from prestigious universities, that cover various concepts across the field of Data Science, such as Coursera, MIT OpenCourseWare, DataCamp, and others. Different educational platforms have different specialities: university affiliated programs generally cover broad introductions to Data Science, online courses like DataCamp allow you to drill down on individual concepts and learn at your own pace, and online programs like StrataScratch focus on how to answer specific technical interview questions drawn from real Data Science job interviews.
Check out our guide on How to Learn Data Science with Python. We also covered 25 Data Science Bootcamps And Courses To Grow Your Career in a previous blog post.
While some data science technical skills and requirements, especially for more advanced and senior data science roles, only come with years of experience on the job, there are still common skills required by most entry-level Data Science roles that any aspiring Data Scientist should make sure they are comfortable with, such as Microsoft Excel, coding languages like Python and SQL, and statistical analysis.
Brush up on the basics to make sure you still remember the ins and outs, or drill down on some specific areas that you are less familiar with, or are interested in building an expertise in. Specialization is important! Remember that Data Science is a very broad field, and general knowledge won't be enough further down the line. As you get further into your career, aim to find a particular part of the Data Science production line and become an expert specialising in that area.
Despite being a primarily technical field, the Data Science field still requires you to be able to market yourself, especially during the recruitment process. In other words, make sure that you've built up an online presence for yourself. While LinkedIn is the baseline standard for every working professional, there are many other avenues that a Data Science aspirant should also establish themself on. Put up projects on GitHub or build out your own website as a data science blog or portfolio. This can help showcase your interests and skills in a particular segment of Data Science, or just demonstrate that you know how to tell a story and communicate your ideas and thought process.
We discussed data science portfolio ideas in a previous blog post.
Making yourself and your work visible online can be a massive boon to your data science job search. In addition to being an additional source of insight to your skills that you can submit with your applications, it can also be an opportunity for recruiters to reach out to you based on the available information, sending job offers that match your skill set and saving you the time and effort of searching for suitable data science job postings. Outside of potential full-time job offers, there can be other consulting or part-time opportunities that may arise. Even further into the future, maintaining this online presence can help you find potential opportunities even when you are not actively searching for them, with recruiters being the ones reaching out to you.
But going back to the most important part: having an online presence means more information about yourself as proof of your skills and experience, and more potential insight on your thought process and working style for companies to check out as you get deeper into the Data Science job search. Let's take a deeper dive into this ultimate guide on how to get a data science job and check the job application process now.
The Data Science Job Interview Process
Most data science companies have their application process split into three parts: the initial application submission, a call with a hiring manager, and then a final round with members of the team, overall spanning the course of a few weeks. The process will vary company by company, with a different overall number of interviews or a longer or shorter timeframe for each data science interview. The circumstances of your application to get a data science job can also affect the application process that you experience, such as if you were scouted by a recruiter, are applying internally, or were introduced to the role by referral.
Note that there will be further disruptions, and likely a virtual setting, for any interview taken during the course of the pandemic.
The first step to the data science job application process is, obviously, submitting your application. When searching job postings, make sure to thoroughly read through each job description and make sure you fulfill the baseline qualifications of the role. Identify the qualities the company is searching for, as well as the organization's product and mission, and decide if your skills and experiences match up to what the role would entail. A good practice is to fully rewrite your resume from scratch after reading the data science job description, using the posting's exact phrasing to describe your work history and past accomplishments.
Many companies also require a cover letter in their data science job postings, though it is usually a good idea to send one along with any application you submit for the additional opportunity to demonstrate your abilities, not to mention just showing the willingness to make the effort and take that additional step. Use the cover letter to briefly introduce yourself and your background, and how your skills and accomplishments align with the role and the company. Going back to your research on the company product and mission, describe why you want to work there or what about the role interests you.
Finally, to get a data science job, include any links to your online presence to your application, such as your LinkedIn, Github, portfolio, portfolio, etc, drawing attention to any particular elements of your past projects that are similar to the responsibilities of the role you are applying to.
HR's Second Round of Eyeballs
If you have the right set of skills and experiences from the initial application in your resume and cover letter that line up with the responsibilities of the data science job posting, you can get through to the next step in the process and talk to real human beings! This often comes in the form of an initial screening call with a recruiter or hiring manager, where you will have a more in-depth conversation about the role and your background. There may also be an intermediate step of a take-home assignment, often in the form of a coding assessment to test your data analysis and coding abilities.
Beyond a few behavioral and technical questions, this call will give you the chance to learn about the role you are interviewing for, such as its relative position in the organization and the team you would be working with. You can also take the opportunity to ask about what the rest of the interview process will look like, and potentially even what kind of data scientist interview questions to expect further down the line.
The final stage of the data science interview usually culminates with a series of interviews and conversations with team members and other people you would be working with. Some companies even ask you to visit their office and do the interviews on-site. There, you will have a more in-depth conversation exploring how you work and your particular skills and background. If there was a coding assessment earlier on in the interview process, you may also have the chance to review your work in the interview to explain your thought process and approach to the given problems.
Expect more specific behavioral questions, as well as longer and more complex technical questions, as you will be physically (or virtually) meeting your interviewers face-to-face. This means you have more of an opportunity to converse and talk through the problems and your thought process. This stage of the interview process with your potential future peers is also your opportunity to get a ground-level insight on what the work and role look like. Take this opportunity to learn about any specifics about the role or responsibility and the broader team and company culture.
How to Prepare for Data Science Job Interview
Interviews are a unique social situation that many may find extremely stressful to deal with. The key to doing well in interviews is to keep calm and to do the proper preparation ahead of time.
Make a plan for the logistics: bring multiple copies of your up-to-date resume, and arrive 10 to 15 minutes early, mapping out your transportation route to the interview location the night before. In the interview itself, remember to keep your answers focused and to the point, directly answering the question being asked. While of course context is important, you also have a limited amount of time in the conversation to create a good first impression. Be mindful to not go off into tangential ramblings.
Do your research on the company itself, and do your best to understand their goals, culture, and product. Be able to describe exactly what the company does in your own words. Understanding the company culture can be key to helping you decide what attire would be appropriate for the interview, whether it be full formal or office casual. In addition, resources such as Glassdoor can give you insight into the structure of the data science interview, and even exactly what questions will be asked.
We talk about the best resources to prepare for data science interviews here.
Types of Questions to Prepare for
Data science interview questions typically fall into one of three categories: business or product sense, behavioral, and technical.
Business or product sense questions cover your understanding of and interest in the company you are applying for. It's important to do your research on the company so you understand what they do, and be able to talk about it, or even offer solutions to potential problems you might see. These kinds of questions can range from the most basic "Why do you want to work here?" to specific scenarios relating to the company's product.
Behavioral questions help the interviewers get a better understanding of how you work, communicate, and handle conflict in a team environment. The most typical questions include a generic ask to introduce yourself, or a runthrough of the resume you submitted. Variants of the "Tell me about a time where…?" are also common. These are often questions you should be able to prepare for ahead of time.
Technical questions cover statistical analysis and coding intensive questions, and are generally the hardest type of question to prepare for. Check online resources, such as Glassdoor, to find questions asked in other interviews for this or similar roles by the company to get some insight on recent technical questions that may be asked. More broadly, the job posting or even public information on the company itself can give you information about the specific skills the interviewers will be looking to test.
Remember that different companies have different standards for what a role actually entails, where some companies might require a stronger focus on code, while others require an extensive statistical prowess or a machine learning focus.
We break down data science interview questions from 80 different companies here. We also cover how to approach both behavioral and technical questions, as well as core statistical concepts with our cheat sheet. Check out our library of interview questions from a variety of well-known companies here!
Taking a look at all of the above, which areas do you need the most brushing up on?
Skills to Brush up on
First and foremost, it's important to establish (or re-establish) your general comfort with interviewing. Particularly for those who might not have interviewed recently, whether due to being recently out of school or having spent a fair amount of time at a previous role, make sure to (re)familiarize yourself with the social setting of a job interview. We've already discussed types of questions to prepare for, and you can use that information to prepare responses to common general questions. It might also be helpful to grab a friend to run through some of those questions in a mock interview setting and give you feedback.
Next, take a moment to think about what Data Science concepts and ideas you have the most experience in and are most comfortable speaking about. On the other hand, which technical concepts from the previous section or even the job posting itself made you a bit more uncomfortable, or that you aren't as confident in answering?
Even for smaller companies that don't have as much publically available data about their interview process, you can still do general practice with specific Data Science concepts with the plethora of online educational resources. We compare different educational platforms in our blog post on the 18 Most Recommended Data Science Platforms To Learn Python and SQL. Use it as you think about which areas you need the most support in.
Questions to Ask During the Interview
Remember that part of the interview process is as much of you interviewing the company in turn to decide if you feel like the company and role is a good fit for you. Prepare some questions accordingly about what you would want out of your workplace, such as the expectations, structure, management, and culture of the company. Early on in the interview process, it's even an option to ask about the structure of the rest of the interview process, including who you would be talking to next and potentially even what types of questions will be asked.
Of course, it's also important to have some questions in response to any information the interviewer gives to you. You can follow up on any answers the interviewer gives for your other questions, ask for clarification about company structure or team environment, and follow up on any projects they mention they are working on.
Finally, remember that you don't need to just save your questions for the end. Interviews are supposed to be a conversation. Especially for any technical questions they ask you, and sometimes even for behavioral or product questions, clear up any uncertainties you have before answering. Asking questions and clarifying any potentially faulty assumptions can be the key to giving a good, comprehensive answer and get a data science job.
Take the time to send a follow-up email thanking each of your interviewers for their time. Outside of just being good etiquette, this is also an opportunity to make your interview more memorable, where you can bring up specific moments in writing that can help your interviewers job their memory further down the line. It can also be your opportunity to put in any additional thoughts, such as parts of your history that you weren't able to bring up in the conversation, or any additional thoughts for a more perfect answer than what you initially said.
Data Science is a rapidly growing profession that has opportunities in every industry, not just in tech. If you're looking to break into Data Science, any previous experience can still carry over. Look into online resources and educational platforms to shore up any gaps in your knowledge, particularly the essentials of Data Science with Microsoft Excel, Python and SQL, and statistics. As you go into the interview process, remember to do your research and thoroughly acclimate yourself to the social setting of interviewing. Good luck!