In today’s post, I want to share my experience working as a data scientist. I know some of my readers are data professionals, and some are still trying to look for data employment.
Nevertheless, I hope that my work experience could enlighten all the readers. It might inspire you, or it might become our point of discussion.
With that in mind, let’s get into it.
My Working Equipment
Let’s start with a simple fact that some people want to know. What is my working equipment?
It’s nothing fancy. My company gave me a Laptop and Phone to do my work, albeit a strong one. Even with remote servers and cloud access, data scientists still need a higher specification to do our work correctly. However, the critical part coming from this equipment is how I could access the data.
I am serious about the data access working equipment because our work is always about the data. Sure, there is a time when the work is about creating an excellent presentation or dashboard, but even then, that kind of work still needs data support.
As data is a sensitive matter, our working equipment control is quite strict. The VPN Access, cloud environment, local server, and database are regularly controlled and monitored.
For any technical data science activity, I use a typical data science tool stack: Python, SQL, Jupyter Notebook, VS Code, GitHub, Airflow, etc. For the cloud environment, I use AWS Cloud.
There are still some smaller pieces of equipment, but that’s it.
Working Schedule
Depending on the company, some companies have flexible hours of working; e.g., you can work anytime as long as you work eight hours a day, but some company has fixed working hours like 9–5 work.
Mine was fixed, meaning that I had a working schedule I needed to adhere to when I was working. I work daily from Monday — Friday, although I am not precisely done 9-5 because as long 8 hours are fulfilled, we are fine.
While it is good that I have a fixed schedule, as a Data Scientist, sometimes we can’t expect to follow this schedule. In my experience, we could expect to start work earlier, but most of the time, we need to finish later, like so later that you take your holiday time. Although, I guess it sometimes happens to every occupation.
I mostly have a hybrid working condition as I still need to go to the office, but it’s pretty flexible, and my schedule is mostly full of remote working.
Working Activities
People want to know more about Data Scientist activities than the equipment or the schedule. Well, here are my activities.
So, half of the weeks that I have were filled with business alignment. Yes, surprising, right? Data Scientists not working with the coding but business?
The remote working conditions make it easier to conduct a business meeting. Just by a touch of a button now, we could conduct multiple meetings. Although why do we need so many meetings? Because this is a regular activity in the industrial situation.
Many of the Data Scientist projects are not based in your favour but on what the company needs. This means we need to have a lot of discussion regarding what the company wants, how to implement the project, the expected result, the timeline, and how the project progresses.
Where is the coding and data part? It comes after that. The other half of my weekly work is coding, data analysis, and modelling; you work hard to learn about it, although it always comes after we agree on the project and the target.
Yes, it is our primary job to analyze data and create predictive modelling, but our responsibility is more than that. While a data scientist’s stigma is working with models and data all the time, you only work with it half the time (at least for me).
For a Data Science aspirant, I always suggest you know the business properly; why? Most of your activities, either in-home or in-office, would be surrounded by business people and the expectation of how your project would help the company business.
I have mentioned above that sometimes my work exceeds the intended schedule. Business people do not understand how long it takes to analyze and create a decent predictive model. That is why, most of the time, the deadline is stricter than expected, and we need to push to do more.
Although I started managing the MLOps part of our department last year and mostly this year, as the data maturity progresses, we expect to have more control over our model in production and the output impact.
Lastly, managing the documentation of the projects is what I did in parallel with the other works.
Working Productivity
Do data scientists have productive work while working from home? I would say yes and no.
Why yes? We could focus on our safe environment without any office distractions to analyze data and do our modelling. Although, why not? As mentioned above, data scientists work with other business people, including your colleagues. While working remotely, it is hard to coordinate and get an explanation regarding the things you need. This could lead to time-wasting just for waiting.
That is why productivity could fluctuate depending on the current condition. Data scientists are still human workers, after all.
Conclusion
That is all a shorter summary of my data science working activity. It’s not the fanciest like other data scientists that I know, but it still provides me with a lot of experience handling data science projects and bringing impact to the company.
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