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I know this guy who is an incredible coder. He picked up Python for his career switch, then quickly stacked on JavaScript, Go, SQL, and a few others just for kicks. And he’s good, too, not just one of those people who put languages on their resume with no data scientist skills to back them up.

But he’s having a hard time getting hired. I met him for coffee a few weeks ago, and our conversation inspired this article. Without wanting to insult him too much, I brought up how his last interview had gone. He’d shown up a little late, he hadn’t sent a thank you email afterward, and while he aced every coding problem, he didn’t engage with the whiteboard questions any further than spitting out a perfectly correct answer.

“Kev,” I said to him, “your coding is unbelievably good. Any company would be lucky to have you as a data scientist. But you need to work on your soft skills.”

Here are the four key soft skills I recommend for every data scientist, whether you want to get into the field, advance in your career, or just do a better job.

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1. Communication

Everyone thinks this means knowing how to talk. It’s the opposite: good communication is all about knowing how to listen, especially in data science.

Imagine this scenario: a stakeholder, maybe a VP of marketing, comes to you with a question about a campaign she wants to run. She's excited about it and has a vision in her mind, but she's not sure how to measure its impact or what data she needs. Instead of immediately diving into the technicalities of how you can pull the data or what models you can use, you first listen. You let her explain her goals, her concerns, and what she hopes to achieve with the campaign.

By actively listening, you can understand the broader context of her request. Maybe she's not just looking for a simple analysis but wants to understand customer behavior or segment the audience in a way she hadn't considered. By listening first, you can provide a solution that's tailored to her actual needs, not just the initial task.

Communication is key in data science. You will not work in a dark basement typing code into a keyboard all day; you will receive requests and have to put together presentations and deal with people. As in data analyst skills, you must know how to communicate to succeed.

2. Adaptability

The StackOverflow 2023 Developer survey is actually a great example of adaptability. The authors introduced for the first time an AI section, showing remarkable adaptability to a changing landscape of development.

AI is just one example. Data science is such a great illustration of that old adage: the only constant is change. To be a successful data scientist, you need to be ready to roll with the punches.

This can mean many different things. The most obvious application is being able to learn new tech easily. Cloud technology is new. AI is new. FastAPI is new. You need to keep up with all of it.

Another application is keeping up with the employment scene. The trend lately is not just to be a data scientist in the traditional sense; many employers expect you to wear many hats. You also have to be a data engineer, a machine learning engineer, and sometimes even a domain expert. The lines between these roles are blurring, and modern data scientists often find themselves juggling tasks that were once siloed into separate roles.

You can also take it to mean understanding and integrating feedback. As data scientists, we often build models or solutions based on certain assumptions or datasets. But they don’t always work as expected. Being adaptable means taking this feedback in stride, iterating on your models, and improving them based on real-world results.

Possibly the worst but most important application is being adaptable to getting fired or laid off.  2021 and 2022 were weird years for labor, with tons of big companies laying off great swathes of employees with little warning. It’s a good idea to anticipate this potential outcome and be ready for it.

3. Teamwork and Collaboration

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Remember how I harped on about communication? Teamwork and collaboration fit into that same bracket. As a data scientist, you do not only work with other data scientists. Everyone loves data-backed anything, so you will be the recipient of any number of requests to produce PowerPoint presentations, reports, and graphs.

To do this successfully, you have to play nice with others. Data science projects often involve working with cross-functional teams, including business analysts, engineers, and product managers. Being able to collaborate effectively ensures that the data science solutions align with business objectives.

For example, in one of my previous roles, the product team wanted to introduce a new feature in our app. Obviously, data was needed to back their decision. They approached me and the rest of the data science team for insights on user behavior related to similar features.

At the same time, the marketing team wanted to know how this new feature might affect user engagement and retention. Meanwhile, the engineering team needed to understand the technical requirements and how the data pipelines would be affected.

Our team became central to this. We had to gather requirements from the product team, provide insights to the marketing team, and work with the engineering team to ensure smooth data flow. This requires not just technical expertise but also the ability to understand the needs of each team, communicate effectively – and sometimes mediate between conflicting interests.

4. Curiosity

I’m taking the cop-out route and not mentioning problem-solving as the ultimate soft skill because I think it’s overused. But honestly, curiosity amounts to the same thing.

As a data scientist, I probably don’t need to tell you that you’ll run into a lot of problems. But at their core, every problem is actually a question.

“Our users aren’t converting,” becomes “How can we make this product more attractive?”

“My model isn’t giving me accurate predictions,” becomes “What can I change to make my model more realistic?” 

"Our sales have dropped in the last quarter," becomes "What factors influenced this decline and how can we address them?"

Each of these problems, when approached with a curious mindset, transforms into a question that seeks understanding and improvement. Curiosity drives you to dig deeper, to not just accept things at face value, and to continuously seek better solutions.

Kevin, from my intro, was a curious person in general. But for some reason when it came to data science, he had blinkers on. Every problem became a nail that had to be solved with a code hammer. And the reality is that not much of data science work can be done that way.

He gave me an example of something he was asked at an interview recently: “The customer support team has been receiving complaints about the website's checkout process. How would you address this?”

Kevin went on in detail about how he’d fix the technical glitch. But the answer his interviewer was looking for was a question like, "Why are users finding the checkout process cumbersome?"

In the real world, a data scientist would need to ask this question to solve the problem. Maybe users from a particular region are facing issues due to a local payment gateway's integration. Or perhaps the mobile version of the site isn't as user-friendly, leading to cart abandonment.

By framing the problem as a question, the data scientist doesn't just stop at identifying the issue; they delve into the 'why' behind it. This approach not only leads to more effective solutions but also uncovers deeper insights that can drive strategic decisions.

Final Thoughts

There are tons of soft skills I didn’t mention here, like empathy, resilience, time management, and critical thinking, to name a few. But if you think about it, they all fall into those brackets.

Communicate with people. Know how to change. Be able to work with others. And approach problems with curiosity. With those four soft skills, you’ll be able to tackle any issue, job interview, or bug that comes your way.
 
 

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.