13 Sep 2022
How To Find The Right Work-Life Balance In Data Science Field
Works at Ekeeda
How To Find The Right Work-Life Balance In Data Science Field?
There doesn’t always need to be a business mindset for everything you do at the workstation. Sometimes, you need to strike a balance between your work and life to create wonderful opportunities for holistic development.
When first started my career as a junior data analyst, my senior colleague insisted that I should consider working more on the technical side justified if there is a clear machine-oriented working form. I knew that there was something wrong with this form of perspective and so I started focusing more on learning and implementing rather than machine-oriented coding for a whole long day.
Now that I’ve led the data science team for a couple of years, I can certainly say to build a high-quality, capable, and forward-thinking team you need some time to be put aside for activities that won’t have business oriented associated with it. Of course, the rest of the time you need to focus on the element. But you need a break for a while from it, if the team has to propel and grow together. This is true at the team level - where there are projects that are dedicated to new technologies and new problems that don’t align directly with working objectives and even at the individual level, where team members need to set enthusiasm to play around with stuff that doesn’t always relate to work deliverables.
If your technical team just focuses on work deliverables, or if you personally find no time to just play around with new technologies or rather say happily embrace the methods, then something needs to be quickly addressed. It will not be a sustainable model for overall job satisfaction and in terms of skill building for the future. Let’s look at a little more depth into using a framework that I found helpful throughout my work experience.
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Client And Data Scientist Co-Relation
Companies' data science teams usually have three major forces at work in the way they add value to the company:
Client Requirements – To meet the client needs that you will serve is always the driving force for the team spirit and is the reason for you in the organization.
Teammates & Data Science Skills have to be applied with the utmost potential in order to develop key solutions for client projects.
Technology and Data Science tools play a critical role in the efficiency and effectiveness of how impeccable solutions get delivered to clients.
Of course, when these three things intersect with each other, you have a robust team who will deliver great data science technologies and solutions to clients. But if your team's time is spent in mere working as robots, then it will create problems for yourself as a data science team lead in the long term.
Thus, it’s a really important thing for building a supercool team culture, balance, and future efficiency and effectiveness.
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The Staff Management
To deploy the right people to dedicated projects is critical thing in running a successful and uninterrupted operation. Projects use solid business cases, but often clients' needs are not clear enough to represent business use cases. Many clients in the company don’t know what are technical terms and thus, need skilled data scientists or other technical professionals to help them understand the possibilities. If your staff is too transactional or only assigns people for well-defined projects, you will lose an important opportunity to widen the scope of your team’s impact on the company.
When you hire account managers or translators to regularly interact with function leaders in companies will have a tremendous impact. It will build skills for people in your team, who over time can contribute with domain knowledge to the process. It will give rise to new and impactful work strategies that will develop interactions that might not have happened in the regular work environment.
The Solution Building
To stay ahead of the technology is like a one-up in the game. Technology development is moving at a break-neck speed. If you think your existing technology is fine and good to settle, then you’ll risk playing catch up when you realize there will at once be something much better coming out in the market. Times need to be reserved for the team to keep in touch with their tech landscape. You should attend hackathons and conferences where they will hear things first. Later, you will need time and budget to do the test and learns with technologies that are potential breakthroughs and prove to be a game changer. With the development pace – mainly in predictive analysis, ML algorithms, cloud computing, and data science packages – I emphasize creating space on a team to be technically sound. One of the best solutions would be it's worth consider having at least one individual who is solely dedicated to technological advancements.
As a data scientist, playing with technology is a crucial part of building expertise and domain knowledge. Personally, I do a lot of data science work and take things as challenges, a game ON and try to bring in fun elements, to prove to myself I can do it. It helps me test the implementation of packages and methods that I’ve not tried yet. I can say truly that I don’t remember a minute of stress due to the fun experimentation that I do on my work project. There is simply no such thing as wasted experimentation or learning in data science.
To have maximum effect and bring productivity to a team, it's important to dedicate projects to just trying out a new technique or approach for a problem, even if it's not guaranteed success.
Motivated and high-performing people want to be doing cutting-edge work and trying new methods, and experimentation is what keeps your team ahead of the game. Teams need to be facilitating as much of this as possible.
The key thing is: What will be the right work-play balance? Business elements can’t be put at risk just for fun. The balance might vary based on the number of work activities, and available resources. Sometimes you will have more capacity or at times you will have lesser work pressure. The important thing is that a good data science team along with the right set of in-demand data science skills will maintain the balance between all work and play. It will not be a constraint for constant and permanent delivery of solutions.
If you lead a team where you can’t maintain balance, then I advise you to get along with the team, take time and talk about how to achieve the goals, and hit the refresh button to revitalize and bounce back with energy. If you work on a team where you feel it's all work & no play, then take time to speak to your data science manager or your colleagues on a serious note about it.
If you want your team to succeed and come under the limelight or rather get appreciated by your clients, and upper management it's important you get the right ‘work-life balance. It is critical to be an impactful and highly motivated team.
No wonder data science is one of the hottest career fields in the market. Millions of aspirants wish to secure spots in top tech companies and start-ups. The job is inspiring yet at times quite monotonous so, it's important you keep up with the technological advancements yet keep yourself light, motivated with the right work-play balance.
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