How to Get a Job in Data Science in a Remote Working World

September 23, 2021
Image of a woman working on a computer with a notebook and iPhone

The COVID-19 pandemic changed how businesses operate and the way people work. When it’s all over, the workforce will probably not go back to how it was before the pandemic—remote work will likely continue its prominence. However, this guide will help you understand how to approach applying for a data science job in-person or remotely and highlight the steadily expanded opportunities for remote data science positions in the United States.

In a recent survey by Flexjobs, 95% of respondents said their productivity levels were the same or higher while working remotely. The same survey found that 65% of the respondents would like remote work to be available full-time after the pandemic, and 31% said they want a hybrid arrangement. Employers are voicing similar sentiments. According to a study done by Gartner, a hybrid approach to work, allowing employees to work remotely some of the time, will become the new normal. Some companies, like Microsoft, have already issued statements saying it will permanently offer employees greater flexibility.

The field of data science is no stranger to remote work. In fact, the position of data scientist is considered one of the most remote-friendly jobs. The roles of data scientist, data analyst, data architect, machine learning engineer, data mining engineer and data engineer are often in such high demand that companies are willing to invest in the best talent, regardless of their location. However, finding a data science job and acing a data science interview remotely can still be daunting.

The University of Virginia’s Master of Science in Data Science degree prepares students for the future of remote work. As Myra Blanchard, Former UVA Assistant Director of Career Advising, reminds students: “I think the biggest thing I want graduating students to remember is that this moment is just a tiny fraction of your whole life story. Know what your values and needs are, and your needs can and will change over time. Let that be your guide as you navigate uncertainties. Your first job out of school, and all the jobs in your life really, make up just one chapter in the story of your life.”

Education needed for a remote job in data science

Aspiring data scientists can gain data science skills in several ways. For example, they might decide to attend a data science boot camp or complete a short data science course. These data science programs provide attendees with some foundation in data analysis and an overview of technical skills, however the depth of learning they provide as well as the weight they carry for hiring managers can’t be compared to that of a graduate degree program. Hiring managers are aware of the time, commitment, andthe in-depth data science knowledge that goes into a MSDS. A short course or MOOC can’t truly compare to a master’s degree which provides students the opportunity to apply technical skills in real-world situations and allows them to learn from top faculty and researchers in the field.

Most data scientists have a master’s degree. A study by Burtch Works found that 91% of data scientists have at least a master’s degree, and 48% have PhDs. Therefore, it’s worth pursuing a master’s degree instead of a short course or boot camp. If you are considering continuing or elevating your career in the field of data science in a remote setting, or cannot take two years to study at an out-of-state university, a master’s degree may seem out of reach. However, this isn’t the only option – online degrees, such as UVA’s online Master of Science in Data Science, give students freedom and flexibility. UVA only requires students to attend one live class a week, usually on a weekday evening, and the rest of the time, they will work through lectures and assignments in their own time.

Online degrees also mirror the experience of working remotely as students will be required to collaborate on data science projects with other students on real-world problems, as they would in a real-world data science team. Due to the fluid locations of team placements in different time zones, regions, or headquarters, online degrees naturally prepare students for success in a remote world of distributed teams.

Students interested in studying data science at graduate level do not need to have a computer science or business analytics background. At UVA, data science master’s students have diverse educational backgrounds such as economics, hospitality management, and history. However, there are some prerequisites to get into the program. For example, students will have to have an understanding of programming language and a knowledge of statistics.

Tips for landing your first data science job

Once you have a data science degree, the next step to working as a data science professional in a remote working world is to get a job. Students can either choose to pursue an internship, immediately apply for data science roles or—if they are already working—transition into a data science role within their organization.

Internships are also a valuable addition to your resume for getting your first data science job. Internships provide valuable hands-on experience solving data-related business problems in real-world environments. If you are in a position to take one, they can even serve as an excellent foot-in-the-door for your top company choice.

If you are applying for a data science job or a data science internship, you will need to go through a multi-step interview process. Data science interviews are usually two-part, an exercise and an interview. The exercise will be a coding challenge, testing your technical skills, and the in-person interview will be used to determine your level of knowledge and if the company can see you fitting in well with the data science team. Prepare to be able to speak to specific examples before the interview; interviewers will want to hear how you could apply your data science knowledge in real-world situations, even if the experience you have occurred in an academic setting.

The top categories of data science interview questions

Prospective job seekers - and competitors for your roles - research the hundreds of potential questions that could be asked in a data science job interview, and questions unique to different companies. Make sure to do your research before an interview on sites like Glassdoor that aggregate past questions from candidates. Below are some general categories of data science interview questions that you can expect:

  1. Statistics questions 
    Data scientists must have advanced knowledge of statistics; this will be critical when doing data analysis and predictive modeling. Therefore, in the interview, candidates will need to show their understanding of statistics. These will be technical questions and could include: ‘What is selection bias, why is it important and how can you avoid it,’ ‘What is p-value?’ or ‘What are the assumptions required for linear regression?’
  2. Programming questions 
    You will be asked about which programming languages you’re proficient in and can work with. Data science professionals are expected to have a good working knowledge of Python, SQL, and R language, as well as Hadoop. You will be asked about the main components of each of these and how they are used in the different stages of data analysis. You may also be asked to explain how data science differs from traditional application programming. The exercise part of the interview will cover solving actual programming questions, but you may also be asked to demonstrate programming knowledge during the in-person interview.
  3. Modeling questions 
    Data modeling and data visualization are essential parts of the data scientist role as they provide the actionable insight companies can use to improve their processes. Here, it is important to highlight what you have done previously, either in a data science project at university or an internship and how you used data modeling techniques to extract value.
  4. Behavioral questions 
    These types of questions are similar to the types of questions they ask in most job interviews. Here, the interviewer is trying to assess your work experience, how well you work in a team, and what you are like as a person more generally. You will need to go over your resume and experience before the interview to remember specific examples of times you have shown leadership, took initiative, and/or resolved a conflict. During this part of the interview, the interviewer may also try to assess if you will be a good culture fit for the company, so may ask about your processes, communication style and passions.
  5. Problem-solving questions 
    Being able to solve problems is one of the core functions of a data scientist. There may not be specific problem-solving questions in the interview, but be sure to explain your thinking when answering all questions to show how you are applying your skills and experience to tackle a problem.

How to build a remote career in data science

A remote career in data science isn’t limited to computer science fields but can be found in businesses ranging from fashion to health. Data science is inherently interdisciplinary. Almost all industries rely in some way on data to make essential business decisions. There are three main job categories in the data science disciplines at growing companies: 1) data scientist, 2) data analyst and 3) data engineer.

Most data scientists start their careers as data analysts, where their core responsibility is to analyze an organization’s data and answer business questions. As they advance, they often become either a data scientist or a data engineer: data scientists often specialize in machine learning and delivering insights or being a data engineer if they prefer building data infrastructure.

From here, data science professionals will start to specialize based on their interests, and these skills translate beyond a physical office. As many of our prospective students pursuing the data scientist route have done, they typically begin as a junior data scientist, progress to senior data scientist and then either become a machine learning engineer (if they are interested in artificial intelligence) or become a lead data scientist (if they are interested in leading a data science team or leading a data science project).

Other data science professionals - some who entered the field immediately out of school or received an advanced degree in a related field - are seeking to advance more quickly into leadership roles. Some will complete their degree and look to lead the data management function, data governance discipline, or business analyst teams in their organizations or elsewhere. Often in these scenarios, they would likely report up to the Chief Data Officer or Chief Information Officer, depending on which stakeholder these teams fall under. These senior-level positions will continue to require a fluency in being able to manage remote workers, and set up systems of success to enable a hybrid working model post-pandemic.

Skills and experience needed to get a job in data science

There is an abundance of data science jobs as the need for data science professionals has steadily increased over the last ten years. Companies are not only looking to hire people with data science skills but are also investing in data programs internally to figure out how to best take advantage of their data and forecast customer decision-making.

Businesses are looking for candidates with a mixture of technical skills and soft skills. They need data science professionals who can handle large amounts of unstructured data using data analytics and predictive modeling to provide actionable insight for the company. The candidates will also need to understand programming language, machine learning, algorithms and artificial intelligence. The soft skills include being able to communicate well and think critically.

Companies are becoming more and more open to hiring a data scientist who will work remotely, as long as they will work effectively in these circumstances. Therefore, graduates will benefit from internships and consulting opportunities from companies employing a hybrid mode. This will allow you to test the waters of remote work before deciding whether or not you’re comfortable in a fully remote position outside of a corporate headquarters or region.

Consider UVA's Online M.S. in Data Science to kickstart your data science career

A data science job starts with a data science degree. UVA’s online Master of Science in Data Science (MSDS) degree program gives students an understanding of how to apply emerging theory to real-world experiences in an online environment, setting students up for the remote working world.

With the Online MSDS, you can keep your current job and study in your own time. The course offers a seamless virtual university experience that does not compromise on face-to-face time with facilitators and other students to build up communication skills, even if it is over Zoom. The course covers all the necessary technical skills needed for a successful career in data science, such as courses in big data analytics, artificial intelligence, data visualization, predictive modeling and data mining.

MSDS graduates have held a wide variety of positions, including senior data scientist, machine learning engineer, data analyst and data mining engineer. Data science professionals who have graduated from the UVA program have gone on to hold positions with some of the top corporations in the US. More information about how to apply can be found here.