What Soft Skills Does A Data Scientist Need to Succeed?

Soft skills are just as important as technical capabilities. At the entry level, the basics are enough. However, taking the next steps in your career require soft skills.

Vin Vashishta | Originally Published: September 19th, 2018

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What the heck are soft skills? It is one of those terms that is often used but rarely understood. Soft skills are those with subjective measures of competence. Are they really necessary? Yes. If you want your career to move beyond the intermediate stage, soft skills are a must.

That is because data science is more than just a summation of skills. There are externalities that impact both the inputs and outputs of data science. Without soft skills, your understanding of what needs to be produced and users’ understanding of how to consume/use those work products is limited. Simply said, without soft skills you can produce data science, but it has limited value to the business.

Many career advice pieces talk around soft skills without any explanation beyond a term and how you use it. It is not instructive to know you need communications skills to interact with colleagues or business acumen to understand the business. What are communication skills? What is business acumen? And what other soft skills do you need?

Communications

There are three main components of communication skills. The first is message creation. How well can you summarize the information you need to communicate into a few high-level points? What are your primary communication objectives for any given interaction?

Most people talk but do not communicate because they do not start the conversation, email, or presentation with any communication objectives. If you are communicating using any medium without communication objectives, you do not really have a chance of being successful.

For a data scientist, message creation is especially important. There is so much we can say about a project or approach. The question is what do we need to communicate about the project to this person or audience?

Who am I talking to and what do they need to know? That is how message creation starts. Refine your message for each project and each stakeholder. Have it ready. That means each message is practiced so it sounds polished. Polished establishes trust. Unpracticed does the exact opposite.

The second component is message discipline. Once you have chosen a message, how well do you stick to your communication objectives? It is easy to get off track or derailed by a question. Message discipline is the ability to avoid those rabbit holes and stay on point.

Finally, there is message retention. The point of communication is to deliver your message. Your target audience retaining that message is a critical success factor. At the end of your communication, how well does the audience repeat back what you have said? Did they receive your message, or did they get something else? Did you present in a personalized way?

Business Acumen

This is one of those terms so often thrown around and almost never explained. Business acumen is an understanding of the governing dynamics of business. What is happening under the hood and why?

It is rarely explained because that is much more complex than my simple definition implies. Why businesses operate as they do is a subject many people spend an entire career to understand. A data scientist cannot be expected to also be an expert in all facets of business.

What a data scientist needs to know is:

What the business produces. What are its internal and external facing product/service lines?

How it produces. How is each product/service sourced, built, and delivered to users?

Who consumes its products? Who are the internal and external users? What are their driving needs?

How the business makes decisions about each of those.

Each of those knowledge points are connected to our work products. What the business produces and how determines the business value of our work products. How does data science support products and services? That question determines a lot about what should be built. This knowledge keeps us from starting a project because it is possible and leads us to projects that are valuable.

Who are the business’s customers and users? Understanding who uses the products we support helps us build solutions that meet the customers’ needs. There are usually several possible solutions to a data science problem. A big part of choosing the right solution is understanding how the solution fits into the internal/external customers’ needs.

Decision support is a big part of what a data scientist is asked to do. We often fall short in this role because we do not have the full picture of how the business makes decisions. There are a lot of soft aspects here, culture, mindset, team dynamics, leadership style, etc. Understanding how decisions are made at multiple levels helps us provide the right data, in the right format, to the right audience, at the right point in the decision lifecycle. Getting any one of those wrong can cause us to be less than effective in our decision support role.

Think differently

Data scientists are not expected to think like everyone else does. We are often contrarians. We challenge assumptions. We introduce new ways of thinking and solving problems. We have a complex toolset that most of the business does not understand. A significant part of our value to the business comes from how different we are.

Difference can lead to friction if not handled the right way. We are often seen as stubborn, obstructionist, ‘always has to be right’, or idealistic. All of those put a damper on collaboration which is critical for us to be successful.

The key is to think differently but act collaboratively. That means being clear about your objectives. You are not prescribing a solution but instead making a different recommendation. You are not contradicting; you are presenting an alternative view. You put forth ideas for discussion rather than issuing an edict.

You need to help the people you interact with see your differences as constructive. Your different way of thinking as valuable. The key is to keep common goals in focus. When everyone is working towards the same thing, being different is seen in a positive light.

Mindset

Mindset is a soft skill we do not talk enough about. What is a mindset? It is our strategy for thinking about and seeing events around us. Our mindset determines how we view and react to what happens to us in the workplace. Two different mindsets are important for data scientists.

In a growth mindset, we look at our capabilities as constantly evolving and growing. The opposite, a fixed mindset, sees our abilities as static. A growth mindset is important for two reasons. In data science, your education is never complete, and setbacks or failures are a constant challenge.

In a growth mindset, you expect to have to learn perpetually. In a field as broad and ever changing as data science, that perception is required. There is no educational finish line in our field, so a growth mindset is the only one that works.

In a growth mindset, failures are temporary and can be overcome. If you can learn anything, you can grow to overcome any challenge. A data scientist often sees multiple approaches fail before finding one that works. The challenges we are given are not easy and often require us to go outside of our experience. The only way we overcome this is by seeing failure as temporary; something we can move past if we work at it.

The second mindset is abundance. An abundance mindset sees enough to go around. That is an important way of thinking in a business. Those who have a scarcity mindset see limited resources. For someone to have enough, someone else must give something up.

The zero-sum game mentality creates a toxic culture in business. Abundance sees enough credit to go around. Users can have exactly what they want instead of picking what is most important while sacrificing in other areas. Abundance is inherently collaborative because it sees outcomes where everyone gets what they need.

 

A complete answer to, ‘What is a data scientist?’ cannot leave out soft skills. The hard skills like programming and math/stats get most of the visibility because there’s so much potential business value there. The soft skills are what turn that potential into reality.

A data scientist in a silo cannot be effective. That is why communication is so important. A data scientist who produces products no one uses produces no business value. That is why business acumen is so important. A data scientist who cannot speak inclusively but persuasively about new ideas and ways of doing things is greatly limited. Finally, a data scientist is either constrained or empowered by their mindset.

It is important to remember that data scientists do not come straight out of school with all these soft skills. It is up to businesses to help them learn each one. When I started out in tech 20 years ago, I did not do any of these well. The companies I worked for knew the value of teaching soft skills to their employees. I greatly benefitted from training and high-quality mentors.