There is plenty of available information about the data science industry. Yet, details about the daily life of a Data Scientist are scarce, because there are so many different types of data scientists. However, determining if data science is the right career path depends on having a clear understanding of what data scientists do in their daily lives.
What Do Data Scientists Do?
Data scientists collect, store, clean, analyze, visualize, and present information and data to a field, industry, or key stakeholders. Granular duties will differ by job title and industry. But in general, data scientists develop insights from data and use those insights to solve problems and complete projects.
Day-to-Day Schedule of a Data Scientist
Daily schedules for data scientists will differ between salaried and freelance positions.
Freelance Vs. Full-Time
Freelance data scientists work on a contract basis, taking on a certain number of clients to fill their days or supplement a full-time job. Some freelance data scientists work more hours than full-time, salaried data scientists if they do freelance data science projects in conjunction with another position. However, self-employed, freelance data scientists choose their projects and the number of hours they work. If you want to work fewer hours with the freedom to choose your projects rather than having them assigned, you may wish to start your own freelance business and create a daily schedule to fit your life and aspirations. You must also be disciplined and able to juggle projects and deadlines.
In contrast, full-time data scientists work for a company or organization as salaried employees with an average work schedule of forty hours a week. Some companies allow more flexible hours and remote work options. Full-time data scientists working in a traditional office environment complete assigned tasks or work on a specific stage of a data science project onsite. For example, a full-time Data Scientist developing a machine learning model to automate dataset organization might spend their day researching the best classification algorithms and writing a task-automation program. Assignments may come from a manager or supervisor who will require regular progress updates and a completion deadline.
Team Vs. Individual
Workplaces that use big data to make decisions, develop products, or complete projects rely on data science teams to work on deliverables. A data science team is usually composed of multiple data scientists or a group of data scientists and industry experts working in collaboration. A project leader or manager develops the schedule for the team, and individuals or partners within the team are responsible for specific aspects of the project while collaborating as needed. For example, one Data Scientist may be assigned to data cleaning, another to analyze the data or prepare results, and a third to create visualizations. The daily schedule for the data science team also includes regular status meetings and using communication channels to check-in or receive assistance and feedback.
A good Project Manager assigns work based on specialization or steps in the data science lifecycle, creating an efficient and unharried team. However, smaller businesses or niche companies may need only one Data Scientist instead of a team. This individual Data Scientist is involved in more facets of a project and will have a busier and more varied daily schedule. Ultimately, data science teams and individuals have schedules that vary—with project stages and deadlines are the determining factors.
Daily Schedule, Experience, and Workplace Culture
Experience and workplace culture affect daily schedules, as data scientists must complete assignments, projects, and deliverables. Novice data scientists with little experience may find daily tasks and deadlines a challenging adjustment, especially without the support and input of a team. Some workplace environments prioritize employee time and arrange assignments that can be completed within the daily schedule. Others prioritize projects and deadlines that can be difficult to complete in a standard workday.
Data scientists who work individually, as freelancers, or in high-pressure environments may feel more pressure and higher expectations to work long hours daily or weekly. And many data scientists thrive in such situations. But if you prefer something more low-key, look for companies that are a good match for your skills and personality and offer a supportive workplace culture that prioritizes a balanced schedule.
Looking for opportunities to test your skills in a workplace environment and reaching out to others in that company is an excellent way to learn more about what that daily schedule looks like for a Data Scientist.
Experiencing the Daily Life of a Data Scientist
Experiencing the daily life of a Data Scientist is one of the best ways to understand the job. Internships provide real-world, professional experiences and an in-depth understanding of what it means to work as a Data Scientist in your chosen field or industry.
If an internship program isn’t feasible, consider informational interviews and mentorships with industry professionals. Informational interviews sound like: you reach out to people in your network or with more experience in the data science industry and ask them prepared questions that will provide detailed information about their jobs and the data science field. Finding a mentor who is a Data Scientist is another way for you to get the inside information you need to understand the schedule and expectations of data science professionals.
Ready to Learn About the Daily Life of a Data Scientist?
The best way to learn more about the day-to-day tasks of data scientists is from industry experts and data science professionals. Besides instruction and training, Noble Desktop's data science classes and programs offer networking opportunities with seasoned data scientists. Programs such as the Data Science Certificate or the Python for Data Science and Machine Learning Bootcamp include professional development opportunities such as one-on-one mentoring and project-based learning. Beginners in the data science industry can use this training to expand their skills while learning more about the daily life of a Data Scientist.