Recent culture shifts suggest that students and professionals prioritize work/life balance when considering a job or long-term career. Therefore, positions with long hours and heavy workloads are less desirable than jobs that value an individual's time and wellness. But data science is a demanding industry with some positions requiring many hours. So, it is essential to understand what to expect when it comes to metrics such as daily schedule and work/life balance.
Average Hours Worked for Data Scientists
The US Bureau of Labor Statistics compiled data that shows full-time workers in the United States tend to work a weekday average of 8 hours a day and 5.35 hours a day on the weekend. That’s 40 hours of work in the workweek (Mondays-Fridays), with up to 11 additional hours worked on weekends to complete tasks or assignments outside the office. However, calculating how many hours a data scientist works depends on whom you ask. Recent surveys of data scientists and analysts show that the average working hours in this field correspond to the general full-time worker data. However, blogs like Data Science Nerd indicate that "data scientists work long hours" and that the 40-hour workweek is only the minimum required hours for data scientists.
The discrepancies in work hours align with the differences in the jobs themselves as well as the differences in company size. As data scientists have become more critical to multiple industries, work expectations also increase. Larger companies have data science teams that include people with various specialties and experience levels. Smaller companies may only be able to afford to hire one data scientist to work on multiple facets of a project. Therefore, in addition to role, responsibilities, and industry, the hours required of a data scientist will also depend on a company’s resources.
Data Science Positions Ranked By Hours Worked
Data science is heavily project-based, so work hours correspond to the project type and industry. The following list focuses on the differences in hours worked based on position, environment, and experience level.
Freelance Data Scientist
The data scientist role that offers individuals the most control of hours worked is a freelance data scientist. Unlike salaried data scientist positions, freelance data scientists can set their hours and expectations. Freelance data scientists interested in part-time work can log 10-20 hours a week taking on projects as desired and choosing the most convenient workdays. Freelance data scientists may also be more interested in diverse projects and willing to work as many hours as necessary. So, working as a freelance data scientist is an excellent option for data scientists who want to choose their projects and control their hours.
Full-Time Data Scientist
Full-time data scientists usually work the standard 40-hour Monday through Friday workweek. Most data scientists have "a good amount of autonomy" in their work, but too much independence may be detrimental to maintaining work/life balance for some employees. Not all workers can finish their tasks in a 9 am to 5 pm work week, leading to overworking. In addition, full-time data scientists reporting to a team or project manager may go above and beyond the average weekly working hours depending on the company's expectations, manager, team, and their own ambition.
Academic or Expert Data Scientist
Data scientists who work in academia or are experts in their field or industry have different hours than office-based data scientists. Data science academics and experts are responsible for completing tasks and project deliverables, but they also do research and disseminate information. Data science scholars teach data science classes, write articles, and manage a lab of data scientists. Data science experts attend conferences and teach workshops in conjunction with their projects and professional output. Depending on their level of achievement and public visibility, they may also be called upon to provide background information for news or media outlets. Data scientists in these research and leadership roles usually work more than 40 hours a week. However, there is a push from within the industry to change culture and workplace habits.
How Data Scientists Maintain Work/Life Balance
Overall, data scientists are generally well-equipped to maintain a good work/life balance. Much of the daily schedule within the data science industry focuses on solving problems, completing assignments, and presenting deliverables to a manager or team. With time and experience, data scientists can learn how long it will take them to do specific tasks and plan accordingly. Less-experienced data scientists may spend hours outside of the office researching and working to complete projects. Off-hours work is par for the course for freelance data scientists, but if you’re a full-time data scientist, track your hours and place caps on how much time you dedicate to their work outside of the office.
Tracking your hours is also useful in determining the average time spent on different tasks. If you find specific duties are taking up too much time, more practice or training may be required. Being transparent with management about your training and what you are capable of completing can help ensure that you stay within your hours. Working consistent hours each week and being accountable to the expectations of your position also helps maintain a good work/life balance.
Interested in Working as a Data Scientist?
Noble Desktop's online data science classes and certificate programs include training in multiple skills and specializations, developing the skills you need for your position of interest. The diversity of data scientist roles allows you to layout your career path, choosing to take on multiple jobs and projects or commit to one company where your job is more focused. The Data Analytics Certificate program covers predictive and prescriptive analytics through business intelligence and data analytics tools. In addition, the Python for Data Science Bootcamp provides hands-on training with real-world datasets and projects by building and evaluating machine learning models. By working on projects and professional development, students participating in Noble Desktop's courses can gain valuable experience as data scientists or analysts.