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Almost everything that we do in our lives right now is data-driven. When we buy from an online store, choose a song to listen to on a streaming platform, or watch a video on YouTube, we generate data. In a day, all the individuals worldwide collectively produce over 2.5 quintillion data bytes. Companies use this to improve their processes to gain more customers or users. Of course, for organizations to benefit from the data we produce, they have to capture it and make sense of it. That is why there is a huge need for professionals in the data science field.
Data scientists can work part-time. There are several companies that are looking for part-time data scientists. However, many experienced professionals in the field explain that it can be difficult to finish all of your tasks if you work for only 30 hours or less a week.
How many hours do data scientists work?
In a nutshell, a data scientist is an expert in gathering large amounts of data, both structured and unstructured, and analyzing, processing, and modeling that data in order to establish actionable plans for organizations.
There are so many tasks involved in being able to gain insights from the significant amount of data generated by users. That is why it can be difficult for a data scientist to work less than 40 hours a week.
Some experienced professionals claim that they can finish their modeling, analysis, and report generating in as few as 15 to 20 hours a week. However, the job does not stop there.
Many claim that the most time-consuming part of the work is coordinating with the people in the company. A data scientist’s day is filled with meetings.
You have to talk to all the people involved regarding what the organization needs, how the projects are going to be implemented, what are the expected results, how are certain projects progressing, and more.
After the meetings, only then can a data scientist get down to his actual tasks of coding, analyzing data, and modeling.
The number of hours that a typical data scientist works would rely on the available infrastructure, how fast the people in the organization replies to his queries, and how fast the organization can implement the changes and improvements suggested.
And just like in any work, even if you are required to work from 9 to 5, you sometimes need to start work earlier, leave work later, or even come in during holidays.
Many professionals confess that even after their 9-5 work, they still have to do some tasks at night just to meet their deadlines. In fact, 50 or 60-hour workweeks are not uncommon for the people in this industry.
Can a data scientist work from home?
Yes, many data scientists can do their work remotely. However, if you are considering working from home as a data scientist, you have to have all the things that you need to do your tasks. Foremost, you need to have access to the data that you need.
At the very least, you must have a reliable computer, Internet access, and VPN aces. You must be able to ensure the safety of the data you are going to access since most of that is sensitive information.
For meeting with your clients, you need to have a good camera, a microphone, and speakers. As mentioned, coordinating with the organization is crucial with gathering, analyzing, and using the data you collect to recommend steps for improvement.
You must also come up with a good schedule so that all the people involved will be readily available should a need to meet arises. Some complaints from experienced professionals state that it takes their clients one to two days to get back to them regarding crucial decisions.
Even if the data scientist performs other tasks during that time, there are still some things that he cannot do unless he gets the response from the people that he works with.
If there are any delays, the data scientist must work overtime just to catch up and stick to the timeline.
This is why working remotely can be extra challenging. If you all work in the same location, you can walk up to the person and demand a response immediately. But if you work in different locations, phone calls, messages, and emails will have to suffice.
Is data science a stressful job?
Just like in any other job, being a data scientist can be stressful. At the top of the list of stressors is the volume of work that they have to finish.
The first task of a data scientist is not to gather data. He must first design a way to effectively gather it. After that, he must sort through everything to find the relevant information needed.
Then, all the years that he spent studying will now be utilized as he applies all his knowledge to understand the data and come up with an action plan for the organization.
At the same time, any progress, setback, and suggestion must be communicated to all the people involved.
Once the project is in motion, the data scientist has to monitor the Key Performance Indicators. This will tell him if the plan is working or if there are any modifications needed. He must report this progress to the people and come up with fixes if necessary.
Besides the number of tasks, there is also the stress of working on a tight deadline. This is made worse if the people in the organization do not have an idea how long it takes to analyze data and come up with a good predictive model.
This is why management often imposes stricter deadlines. Therefore, the data scientist has to spend extra time working just to hit the targets in a timely manner.
The time constraint is compounded by the difficulty of staying in contact with all the people involved.
Finally, since the industry is evolving, a data scientist must spend time learning new technology. He must keep up with any new developments in the field of data science as well as the industry that he is consulting for.
All these factors combined can cause any professional stress. They are external factors that he cannot control. The only thing a data scientist can do is learn how to adapt and find the right resources to help him do tasks effectively.
Is Data Science a Dead-End Job?
While the need for data scientists is going through the roof at the moment, there are some advancements in technology that are threatening to turn it into a dead-end career.
The emergence of automation tools will take away some part of the job. Data scientists would no longer have to be there to select which model to use for a given set of data.
They also would no longer need to be a part of the data validation process.
However, the thinking part will still be done by the data analyst. They will still need to manage the data, think up of proper ways to test if the plan is working, and even feed the AI robot the correct input to come up with the needed results.
In short, the number of job posts may go down, but the work will still be there. What can happen is that the field will be broken down into specialties. This is where the data scientist will need to focus on the analytics and machine learning part of the job.
That is why it is important to continue to improve your skill and expand to other fields when you are working as a data scientist.
You will not end up with a dead-end position because computers and robots cannot make better inferences than humans do. Additionally, they will not be able to program themselves as effectively as people do.
The only time that you will get stuck in some sort of repetitive dead-end AI or data analytics job is if you do not continue to learn.
You can work part time as a data scientist, but it will be difficult, especially if you are working with a huge company. You can also work remotely as long as you have the necessary equipment at home to do all of your tasks.
Finally, you are assured that data science is an ever-expanding field. While the job title might eventually lose its glamor, you will always have work as a data scientist.
The key is to keep abreast with the technological advancements in the field and learn more about the other fields that you are involved in.