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I know this guy who is an amazing coder. He chose Python for a career change and then quickly applied JavaScript, Go, SQL, and a few others just for fun. And he’s also good, not just one of those people who includes languages on his resume without data scientist skills to support them.
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 mentioned how his last interview had gone. He was a little late, didn’t send a thank you email afterwards, and although he overcame all the coding problems, he didn’t engage with the whiteboard questions beyond spitting out a perfectly correct answer.
“Kev,” I said, “your coding is incredibly good. Any company would be lucky to have you as a data scientist. But you need to work on your interpersonal skills.”
These are the four key soft skills I recommend for every data scientist, whether you want to enter the field, advance your career, or simply do a better job.
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Everyone thinks this means knowing how to speak. It’s quite the opposite: good communication is about knowing how to listen, especially in data science.
Imagine this scenario: An interested party, perhaps a VP of Marketing, comes to you with a question about a campaign they want to run. She’s excited and has a vision in mind, but she’s not sure how to measure its impact or what data she needs. Instead of immediately diving into the technical aspects of how to extract the data or what models you can use, listen first. You allow them to explain their goals, their concerns, and what they hope to achieve with the campaign.
By actively listening, you will be able to understand the broader context of your request. Maybe you’re not just looking for simple analytics but want to understand customer behavior or segment your audience in a way you hadn’t considered. By listening first, you can provide a solution that fits your actual needs, not just the initial task.
Communication is key in data science. You won’t be working in a dark basement typing code on a keyboard all day; You will receive requests and you will have to prepare presentations and deal with people. Like in data analyst skillsYou must know how to communicate to be successful.
The StackOverflow 2023 Developer Survey is actually a great example of adaptability. The authors presented for the first time. ai” rel=”noopener” target=”_blank”>an ai sectionshowing remarkable adaptability to a changing development landscape.
ai is just one example. Data science is a great example of that old saying: the only constant is change. To be a successful data scientist, you must be prepared to roll with the punches.
This can mean many different things. The most obvious application is being able to learn new technologies easily. Cloud technology is new. ai is new. FastAPI is new. You need to stay up to date with all of this.
Another application is to keep up to date with the job landscape. Lately the trend 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, 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 previously isolated into separate roles.
You can also interpret it to mean understanding and integrating feedback. As data scientists, we often build models or solutions based on certain assumptions or data sets. But they don’t always work as expected. Being adaptable means taking this feedback in stride, iterating your models, and improving them based on real-world results.
Possibly the worst application, but the most important, is the ability to adapt to being laid off or laid off. 2021 and 2022 were strange years for the workforce, with tons of large companies laying off large numbers of employees without notice. It is a good idea to anticipate this potential outcome and be prepared for it.
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Remember how I insisted on communication? Teamwork and collaboration fit into that same group. As a data scientist, you don’t just work with other data scientists. Everyone loves anything data-driven, so you’ll be on the receiving end of any number of requests to produce PowerPoint presentations, reports, and charts.
To do this successfully, you must be kind to 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 data science solutions align with business objectives.
For example, in one of my previous roles, the product team wanted to introduce a new feature to our app. Obviously, data was needed to support their decision. They approached me and the rest of the data science team to gain insight into user behavior related to similar features.
At the same time, the marketing team wanted to know how this new feature could impact user engagement and retention. Meanwhile, the engineering team needed to understand the technical requirements and how the data channels would be affected.
Our team became fundamental to this. We had to gather requirements from the product team, provide information to the marketing team, and work with the engineering team to ensure smooth data flow. This requires not only technical expertise but also the ability to understand the needs of each team, communicate effectively, and sometimes mediate between conflicting interests.
I’m taking the avoidance route and not to mention Problem resolution 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 will run into a lot of problems. But at its core, every problem is actually a question.
“Our users are not converting” becomes “How can we make this product more attractive?”
“My model is not giving me accurate predictions” becomes “What can I change to make my model more realistic?”
“Our sales have fallen in the last quarter,” he asks. “What factors influenced this decline and how can we address them?”
Each of these problems, when approached with a curious mindset, becomes a question that seeks understanding and improvement. Curiosity leads you to go deeper, to not just accept things at face value and to continually look for better solutions.
Kevin, from my introduction, was a generally curious person. But for some reason, when it came to data science, he had blinders on. Each problem became a nail that had to be solved with a coding hammer. And the reality is that not much data science work can be done that way.
He gave me an example of something he was recently asked in an interview: “The customer service team has been receiving complaints about the website’s checkout process. How would you approach this?
Kevin explained in detail how he would fix the technical issue. But the answer the interviewer was looking for from him was a question like: “Why do users find the payment process cumbersome?”
In the real world, a data scientist would need to ask this question to solve the problem. Perhaps users in a particular region are facing issues due to the integration of a local payment gateway. Or maybe the mobile version of the site isn’t as easy to use, causing cart abandonment.
By framing the problem as a question, the data scientist is not simply identifying the problem; 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.
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 between those parentheses.
Communicate with people. Know how to change. Be able to work with others. And approach problems with curiosity. With those four soft skills, you will be able to tackle any problem, job interview, or mistake that comes your way.
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.