The Importance of Soft Skills in Data Science Careers

As a data scientist, you might spend your days analyzing data and building models. But while many professionals may have expert technical skills like data visualization, soft skills will help distinguish you as a rising leader in an organization. In fact, companies have been prioritizing the hiring of data scientists with soft skills because they view leadership potential as essential to successful innovation.

Soft skills for data scientists include communication, critical thinking, problem solving, teamwork, and empathy. These critical soft skills aren't usually taught in school or as part of training, but they can be developed over time and are essential for your success. Within the first few minutes of an interview or meeting, your peers will be able to tell if you have the personal attributes and communication skills to succeed in data science.

This blog explores the top soft skills aspiring data scientists need to thrive in their careers, ethical considerations in the industry, and stories of successful leaders in data science.

Understanding Soft Skills in Data Science

As a data scientist, it’s easy to get stuck in a silo mentality, especially if your job focuses on independent tasks like number crunching and analyzing data all day. But having the right mix of technical expertise and soft skills can give you a competitive edge over other job applicants and increase your odds of thriving in a new role. Workforce futurist and author Alexandra Levit says that soft skills can provide “career durability,” which is crucial for navigating turbulent job markets.¹

Soft skills are defined as a set of behaviors, personality traits, and work habits that help you bring value to your organization. They are important in every industry, but the most important soft skills in data science are communication, teamwork, dependability, and problem-solving. These skills will help you take complex technical concepts from concept to completion with clearer understanding and collaboration among teammates.

If you feel like your soft skills are lacking, the good news is you can cultivate them. When interviewing for new roles, ask about how teams collaborate and what soft skills or leadership training programs are available.¹ Take advantage of learning opportunities on the job to collaborate, regularly practice actively listening, and be open to new perspectives. If you are in a management role, these soft skills will help you build relationships with a variety of stakeholders, support innovation at your organization, and nurture the next generation of talent. You never know: the idea for a best-selling product or tool could come from conversations with a new team member, senior leader, or even an intern.

Communication Skills

Imagine you're leading a team of data scientists, cloud engineers, and executives. They all come from diverse backgrounds, different knowledge levels, and have competing priorities. How can you help bridge the gaps and ensure everyone is on the same page?

Communication is the glue that holds everything together and distinguishes high-performing teams from the rest.² When there is a clear understanding of who is involved, what needs to be done, and by when, you'll have a much easier time reaching your goals. Strong communication skills include transparency, frequency, active listening, asking questions, and following up afterward. With these tactics, you'll be incredibly effective in one-on-one settings, but also when you need to present data (which often has lots of technical jargon and complex ideas) to less-experienced parties. Being able to clearly explain your ideas and align multiple parties will serve you well in a variety of data science projects.

If you’re engaging with cross-functional teams, it’s important to update other team members regularly on progress and seek input from non-technical stakeholders. In an constantly evolving field, data scientists must share knowledge and engage with the community through conferences, seminars, and open-source projects, which underscores the continual importance of effective communication in maintaining professional growth and collaboration.^2,3

Problem-Solving Abilities

As a data scientist, you can leverage your skills in statistics, machine learning, and data analysis to extract meaningful insights and solve complex problems across a variety of industries.⁴ Once you understand a business problem, you can break it down into a process flow and identify the necessary data and artificial intelligence (AI) techniques to solve it. The process might start with thought experiments and modeling techniques, which are then used to help clarify a company’s most important goals.⁴ Stakeholders can use data insights to inform business decisions or shape social and health policies that impact communities.

Here are some technical tools you can use to reinforce your problem-solving skills:⁵

• Predictive analysis: Using historical data, such as stock prices, disease outbreaks, and customer behavior, to predict future outcomes
• Fraud detection: Developing algorithms to detect unusual patterns to help prevent unauthorized or fraudulent transactions
• Recommendation systems: As Netflix and Amazon have done, this involves using algorithms that analyze user behavior and tailor products and content suggestions, which can result in a better user experience and higher sales
• Climate modeling: Analyzing large datasets to model and predict climate patterns, helping researchers address environmental impacts on communities in specific geographic locations

Teamwork and Collaboration

If you’re a member of a data science team, chances are you have a leader who oversees critical tasks and projects for those beneath them. Just like on any other team, though a data scientist’s ability as an individual is only as good as what they accomplish with others for the good of the business. Beyond daily teamwork, you might collaborate with peers from other teams, leveraging diverse perspectives to come up with an innovative product or service. That means offering and receiving constructive feedback, asking other team members to share their thoughts and expertise, and delegating when necessary.

Empathy and Customer-Centric Approach

Empathy, a key building block of effective teamwork and collaboration, requires practice and a deeper understanding of your emotional intelligence. Business leaders agree the benefits are worth the effort: Nearly 84% of CEOs believe the ability to understand another person’s perspective is essential to better business outcomes.⁶ In addition, you can’t attract customers and expect your business to retain them without this critical leadership trait. The more you can empathize with end-users, stakeholders, and decision-makers, the better you can align your data analyses with real-world needs.⁷

Follow these steps to cultivate empathy:⁷

• Actively listen to others
• Ask open-ended questions
• Seek out feedback
• Practice viewing situations from diverse perspectives

Ethical Values and Integrity

In the world of data science, privacy and ethical concerns have already been the subject of intense debate. With the amount of data that the world produces expected to reach 463 exabytes by 2025, ethics around data collection, insights, and use will continue to pose challenges.⁸ While data scientists have an important role to play in these conversations, the development of data framework policies must involve all company stakeholders, including marketing, legal compliances, and the executive suite.⁸

When designing AI-related systems, data scientists can take the lead in asking the right questions so that ethics doesn’t take a backseat to concerns about profit margins. In industries like health care, data scientists have designed disease prediction technology to complement the expertise of doctors, not replace it.⁹ In doing so, consideration is given to how these systems meet business needs without sacrificing the care and counsel given to patients.

Case Studies and Success Stories

One hallmark of a data science leader is the ability to communicate complex topics for non-technical audiences. With 20 bestselling books and a Forbes column to his name, Bernard Marr stands out as a globally recognized thought leader, technology futurist, and sought-out speaker.¹⁰ Attendees of his workshops marvel at how well he breaks down topics, such as performance management, big data, and analytics into understandable terms for many audiences.¹¹

Another key trait is the ability to influence others and drive innovation. Entrepreneur and scientist DJ Patil does both. Known for his agile project management style, technology acumen, and integrity, he has spent much of his career molding data into innovative products for companies like eBay and LinkedIn. Motivated by the belief that data can save lives, Patil excels at building relationships with multiple stakeholders to solve problems. He was able to do just that when he served as Chief Data Scientist at the White House during a time of intense mistrust between law enforcement and the public. Under his leadership, law enforcement, technologists, and activists collaborated on what is now known as the Police Data Initiative and Data-Driven Justice Initiatives, which supports police departments serving 94 million Americans.¹²

When it comes to women in data science, Katherine Johnson may be one of the most famous leaders to date. She blazed the trail for female data scientists by making significant contributions to the NASA space program during the 1960s, which are highlighted in the film “Hidden Figures.” She is credited with using data to calculate a perfect path for Freedom 7, the spacecraft that put the first astronaut in space.¹³

If you pursue a data science career path, you will have many opportunities to cultivate soft skills, work on interesting projects, and make an impact like these leaders did.

Prepare to be a Data Science Leader

Most data science roles will have as much emphasis on hard skills (machine learning, programming languages, exploratory data analysis) as they will on the soft skills we've discussed. When you're looking for advanced education options, consider the significant impact that a master's degree would have on your career.

For over 70 years, the sole mission of New York Institute of Technology has been to create career-oriented, technology-focused degrees. The Online Data Science, M.S. at New York Institute of Technology has the flexibility for working professionals who want to advance in their career without interrupting it. Within ten months, you’ll have the critical knowledge, leadership skills, and experience you need to gain an edge in the field.

If an advanced, future-focused data science education sounds right for you, set up some time to talk with an admissions outreach advisor.