As a senior principal data scientist at Navy Federal Credit Union, Rahul Zende uses the predictive power of data to build solutions that enable better organizational decision-making and deliver improved service to members. 

“As much as we can anticipate our members’ needs ahead of time, that is for the better,” Zende said. 

For example, he builds predictive models to anticipate when members might be in the market for a car loan. Knowing that helps the nonprofit credit union reach out with strategies to help those with lower credit scores boost their chances of qualifying.

“Then when it comes time and they need a car, they’re more likely to be approved to be able to buy one,” he said.

In the data-driven economy, organizations rely on data science professionals like Zende (pictured at top), an alum of the University of Washington’s Master of Science in Information Management (MSIM) program. Data scientists are in demand because they have the skills to manage, analyze and apply complex information to strategic decisions. The growing need for data literacy and advanced analytics means that professionals must possess not only technical expertise but also a deep understanding of how data informs business, policy and society. 

Data scientists consistently rank at the top of the U.S. News & World Report's Best Jobs in America list. The U.S. Bureau of Labor Statistics concurs, placing data science among its fastest-growing occupations. The job market for data scientists is expected to grow by 34% between 2024 and 2034, reflecting rapid expansion across sectors including health care, finance, government and education. As companies and industries increasingly prioritize data-driven decision-making, the demand for professionals like Zende with the right education, experience, skills and knowledge will continue to rise.

“Data science is suited for someone who wants to keep learning,” Zende said. You have to have curiosity and you have to keep updating yourself because techniques evolve over time.”

What is a data scientist? 

Zende pursued his MSIM to build on his previous work as a data engineer for Wells Fargo by adding skills in machine learning and data science. As a data scientist, he can help an organization identify patterns, answer complex questions and build predictive models to anticipate future trends. Using a blend of AI, statistical analysis, programming and data visualization, data scientists translate complex information into actionable knowledge.

As the profession evolves, distinctions between related roles have become increasingly important. Although job titles and day-to-day responsibilities can differ across companies, the qualifications for these roles tend to share common ground. Most require a solid background in statistics, programming and data management, along with strong communication skills to clearly explain insights and results. 

Here’s how data scientist jobs compare with some related roles:

• Data engineer vs. data scientist: Both require a high level of technical proficiency, but their areas of focus differ. Data engineers focus on building and maintaining systems that move and store data, making sure information is organized and flows smoothly. Data scientists use that data to identify patterns and insights that inform business or research decisions. 
• Data analyst vs. data scientist: Another data-related profession, data analysis typically focuses on assessing datasets and identifying trends. A data scientist is more likely to apply advanced techniques and conduct deeper statistical analyses. 
• Informatics vs. data science: Informatics typically focuses on the application of information technology and data management within specialized domains such as health informatics or legal informatics. Data science overlaps with informatics in some areas, but has a broader reach, applying analytical techniques such as statistical modeling and machine learning.

What are data scientists' job duties?

Although the job responsibilities for a data scientist can differ depending on the company, industry, level of experience, job title and scope of work, typical responsibilities include:

• Collecting and preparing large datasets for analysis
• Performing exploratory data analysis (EDA) to uncover trends
• Developing and testing machine learning models
• Interpreting and presenting data to technical and non-technical audiences

Data scientists are creative problem solvers who ask questions, create hypotheses, and use data to test and refine their ideas. They often collaborate closely with engineers, product managers and domain experts to ensure their analyses address real-world challenges and drive meaningful outcomes. 

“I interact with folks throughout the lifecycle of a project,” Zende said. “I speak to business colleagues, sometimes our data engineering counterparts as well.” He addresses questions such as, “‘What really are we trying to solve?’ And ‘Do we have enough data to solve it?’” 

Industries and organizations: data scientist positions  

Data scientist positions can be found in virtually every industry. Whether embedded in finance, like Zende, or research teams, corporate strategy units, product development groups, marketing departments or customer experience teams, data science jobs play a central role in how modern organizations evaluate risk, improve outcomes and identify opportunities.

In health care, data scientists may develop machine learning models to predict patient readmissions or optimize clinical workflows. In higher education, they may help analyze enrollment trends or model student performance over time. In the media, they often focus on recommendation algorithms, sentiment analysis and audience segmentation. The rise in remote data scientist jobs has made all of these jobs more accessible, often allowing these professionals to achieve a better work-life balance. 

As the profession matures, so do the roles. More companies are looking for candidates for senior data scientist jobs, including data science manager jobs and data science director jobs. These positions require not just technical expertise but leadership, vision and the ability to scale a team. A graduate degree can help people build the experience and skills that often lead to advancement into leadership positions.

How to become a data scientist: professional pathways

Pursuing a data science career typically requires a combination of formal education, practical experience and continuous skill development. Many professionals enter the field with backgrounds in computer science, statistics or mathematics, others transition from related areas such as engineering or business, and many come from fields such as health care, retail, manufacturing where they see an unmet need for data science skills.

Professionals enter the field of data science at various stages in their careers. A marketing specialist may seek to strengthen data analysis skills, while someone with a programming background might aim to advance to a leadership role. The MSIM program, offered online and on the UW’s Seattle campus, offers flexible degree tracks that enable students to tailor their education to their experience, career goals and preferred timeline.

Data scientist education requirements

While it is possible to enter the field through boot camps or certificate programs, most employers prefer to hire data scientists who hold academic degrees. In fact, according to 365DataScience, more than half of all data science job postings in 2024 required a graduate degree. Advanced training is especially important for senior or specialized positions that demand not only technical expertise but also a deeper understanding of strategy, data governance and leadership. 

Many professionals who Google “What degree to be a data scientist?” often discover that success in the field requires both technical expertise and strategic thinking. The UW MSIM program meets this need with a data science specialization that prepares students to assume the job responsibilities for a data scientist, training them to transform complex data into actionable insights and lead data-driven decision-making.

Data science skills and knowledge

Succeeding in the dynamic field of data science requires a blend of technical skills, knowledge and characteristics that enable professionals to solve complex problems. A data scientist needs to be innately curious and willing to be proven wrong, Zende said.

“You don't want to be too married to your past assumptions,” he said. “You want to be willing to find data that proves you wrong and correct your assumptions accordingly.”

Some of the most in-demand data scientist skills and areas of expertise include:

• Big Data
• Business acumen
• Cloud computing
• Data ethics
• Data visualization
• Deep learning and natural language processing
• Machine learning and artificial intelligence
• Programming in Python and R
• Statistics and mathematics
• SQL and NoSQL

Key characteristics that support long-term success include:

• Adaptability in fast-changing environments
• Clear communication skills
• Collaboration across technical and non-technical teams
• Environmental and social awareness
• Intellectual curiosity
• Strong analytical thinking

The UW MSIM program develops these competencies through both foundational and specialization courses. The core curriculum engages students with topics such as policy and ethics, analytical methods and strategic leadership. The data science specialization covers exploratory data analysis, statistical inference, supervised and unsupervised machine learning, scaling and distributed computing, and network analysis. 

Students gain real-world experience through internships, applied projects, and other experiential opportunities. Many MSIM students complete an industry-sponsored project as their required Capstone or Practicum experience, applying skills that align with the job responsibilities for a data scientist — such as data analysis, strategic thinking and systems design.

Zende interned at Seattle’s Fred Hutchinson Cancer Research Center during his time in the MSIM program, advising on a project to overhaul and consolidate Fred Hutch’s legacy database systems. That led to a job at the UW’s Institute for Health Metrics and Evaluation, where he continued to develop his skills. “Being able to work on real projects challenged me and my understanding of data science and the underlying techniques,” he said. “I think that I grew by leaps and bounds.”

Take the next step: earn your MSIM at the University of Washington

The demand for skilled data scientists continues to grow across industries, offering opportunities for professionals to start and advance their careers in this exciting field. For those interested in higher-level, higher-paying positions in data science and information management, an advanced degree is often essential. 

To fast-track your career in data science, consider enrolling in the UW’s MSIM program. The skills and real-world experience gained through this master’s degree can qualify you for senior positions and help you stand out in a competitive job market. Request more information, contact an advisor to explore your options, or, if you’re ready, you can start your application now.