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As a Data Scientist, your number one aim is to be able to write effective code that can be applied to the organisation's goal. 

However, many other attributes are important to hiring managers when taking on new Data Scientists that are sometimes heavily overlooked. Soft skills are just as important to hiring managers as hard skills. I will be going through the ones that I believe are the most important. 

1. The Willingness To Learn

 
A lot of hiring managers want people who have the right skills for the role but also have the ability to adapt their current skills and develop new skills. 

We never stop learning, especially when we’re in a field that is highly technical and new tools are being released, and older ones are being improved. New algorithms are constantly being introduced, therefore if you are not willing to learn; it can be very frustrating for your team manager. 

Imposter syndrome is real and affects a lot of people in various fields. It can also act as a blocker in the success of your career as you will constantly be doubting your abilities because your main goal is to achieve. Concentrating on the skills you currently possess and how you can develop them by learning new skills is one way to overcome imposter syndrome. 

How you will be tested in an interview?

 
Many hiring managers, regardless of the field you are in will sometimes ask you this question:
“Was there ever a time you felt that you lacked a particular skill, what happened, and what did you do to resolve the issue?”

This will help hiring managers to understand how you handled a lack of skills in a particular project or your previous roles; proving if you were willing to learn or if you accepted that you didn’t know the skill and continued the same. 

Using your spare time out of your working hours to learn new skills or develop current ones is another way to prove to the hiring manager that you are willing to learn. There are various platforms where you can achieve this as a Data Scientist, such as Udemy, Coursera, edX, and more.  

2. Relating Code To The Business

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Although coding is one of the main requirements for any Data Scientist, being able to understand and apply it to the business and its goal is equally important. 

Having a full grasp of the task at hand and how it relates to the business, will allow you to code better. It will prove to your manager that you understand what is required to benefit the business, rather than just completing a task because you were asked. It differentiates if you are an active member of the team and the business as a whole. 

Through this, you as a Data Scientist will have better skills and understanding of the problem at hand than a CEO or Managing Director with little technical skills does. You will be able able to use your hard skills to direct the business in the right way, through your coding and business acumen.

How you will be tested in an interview?

 
During a Data Science interview process, you will be asked to undergo a technical phase. The hiring manager will ask you to handle a technical test in a way that is unique to the business.
 
For example, if the role you have applied for is a Data Scientist that has expertise in Machine Learning models, the technical test may be related to different modeling solutions for a particular problem in the business. 
This will prove to the hiring manager that you have the ability to apply your coding skills to the business’s goal. 

3. Identifying Problems And Proposing Solutions 

 
As mentioned above, there may be people in your team or higher up in the business who have little technical skill and understanding. Being able to identify problems in the company's current problems or chosen solutions, are proposing your own will make you stand out. 

This proves to the hiring managers that are you willing to help the business improve, and when you get the job it will prove to the company as a whole that you are a valuable asset to the success of the business. 

It is easy to say ‘yes’ to every task thrown at you. But it is harder to say ‘I don’t think this is the right solution, how about trying X?’. A lot of businesses are failing to strive as they lack adopting other team members' suggestions and solutions. 

There is no harm in putting your suggestion forward, the worst thing that can happen is that you are wrong or your manager gives you a better understanding of why the business cannot use that particular solution. In the end, you’re still learning. 

However, if you chose to keep your ideas to yourself, you’re doing yourself more harm than good. Employers will believe that you are not an active employee and you’re not willing to contribute to the success of the business. 

How you will be tested in an interview?

 
It is difficult to test this soft skill during the interview stage, as you are limited on time. However, technical interviews do have more than one phase, so you can be tested on the skill anytime during each phase. 

 
A typical question hiring managers ask is:

“Was there ever a time that you identified a problem with the task at hand and had your own solutions? If yes, what did you do?”

 
Go in-depth with your answer, as hiring managers will know if it is superficial or not. 

4. Ask Questions

 
The majority, if not all, hiring managers will ask you if you have any questions. Ask, ask, ask! If you don’t ask, you don’t get. 

You applied for the role because you want the job. However, you also need to make sure that this is the right role for you. The hiring manager will be vetting you, so you should also be vetting the company to see if the company's values and requirements are something you are willing to commit to.

The questions can be:

• What will my typical day look like? 
• Does the company offer training?
• Does the company help with self-development during my employment?
• Can I get a bit more understanding of the benefits in the vacancy description?

Conclusion

 
The hiring process for a Data Scientist is not the easiest. There are different phases and they all require heavy technical knowledge and test your soft skills. 

 
If you would like to know more, have a read of:

• Data Science Interview Guide - Part 1: The Structure
• Data Science Interview Guide - Part 2: Interview Resource

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.