A Glimpse into the Professional Day-to-Day in the Life of a Data Scientist

 

Thinking of becoming a data scientist, and are wondering how your days would look like if you became one?

 

So, here in the below article, you will be able to learn and look closer into the life of a data scientist and how they might go about their days by getting to see how they navigate through a professional day as a data scientist.

 

Morning Routine

[ Kick off with some coffee, code, and collaboration ]

 

8:00 am - 9:00 am: Typically, the day of a data scientist tends to start in the early hours of the morning with a cup of coffee, tea, or their preferred refreshment. And is followed by a look into their phones assessing the day’s work and further learning about any new or recent developments and trends in their field.

 

9:00 am - 10:00 am: Working in the data science profession it is important to communicate with your team and colleagues regularly to keep everything running smoothly. Therefore, the mornings of most data scientists begin with a group meeting of the team to talk about what’s going on, get ideas, and make sure everyone is on the same page.

 

10:00 am - 12:00 pm: Once the team meeting comes to an end, typically data scientists set out to tier cabin or desk to plan their work for the day. They probably have a fresh pile of collected data on their agenda for the day to sort, organize, and address the data.

 

Afternoon Routine

[ More analyzing, modeling and brainstorming awaits ]

 

12:00 pm - 1:00 pm: This is usually around the time that data scientists tend to break for lunch. Head out to have lunch with your colleagues for a relaxed break time of discussions and planning.

 

1:00 pm - 3:00 pm: Post the brief lunch session with colleagues, a data scientist is expected to sit and work on the core parts of their job description which includes, working with algorithms, machine learning models, developing parameters and bringing out insights to improve company performance and further analysis and working according to the day’s requirements.

 

3:00 pm - 4:00 pm: Further down the day, data scientists find themselves continuing the day’s work while making sure to keep a consistent check and communicate with the other respective departments and update their findings through meetings such as stakeholder meetings and progress updates with the heading group of the company.

 

Evening Processes

[ powering through the day with some closing meetings, and reflections ]

 

4:00 pm - 6:00 pm: As the work time nears the end of the day, data scientists continue to work on their assigned tasks, while compartmentalizing the next day’s work while finishing up with any discussions, or follow-up meetings they have attended on that day.

 

6:00 pm - 8:00 pm: From about 6 to 8 pm depending on the closing hours of the office of different data scientists, they simply sit back and reflect on their day’s work and figure out what has to be completed or done in the coming days, before logging out of their work day in the office.

 

Beyond the 9 - 5 work time,

 

By the clock striking 5 to 6 pm, most data scientists wrap up their day-to-day work obligations and discussions and move any incomplete work to the next day. But sometimes it so happens that the 9-5 work time of a data scientist might extend further into conferences, meetings, attending hackathons or conferences from the side of the company. This falls under the job purview of data scientists as they form an important part of the decision-making process and team of any company or organization.

 

Therefore, in Conclusion:

 

Remember that the life of a data scientist may be carefully organized and flow in an order, but it still differs depending on every individual and how they prefer to work. This article only highlights the general work-life procedures followed by the average data scientists in companies. You may choose to work differently or be required to work differently depending on your company's work timings. the project you choose to work on, your everyday routine, and such conditions of a personal nature that you have to factor in to achieve your work-life balance as an aspiring data scientist.