Data scientists are leading the way in how companies operate and make decisions. They apply statistical analysis, as well as computer and information science, to large sets of information. By pursuing this field, you could use scientific methods to examine and analyze vast quantities of data. And if you enjoy a challenge, 25% of entry-level job descriptions in the field requested applicants have a master’s degree.
A career in data science is resilient in the face of automation. You’ll be leading changes that could affect marketing, sales, operations, project management, business intelligence, and much more.
Big data is everywhere. Businesses, nonprofit groups, and government agencies have access to unprecedented volumes of data, and if they’re able to properly manage, interpret, and act on this information, they’ll have a huge advantage over their competitors. A McKinsey survey conducted in December 2018 found that 47% of respondents said that data and analytics had altered the competitive landscape in their fields, a sizable increase from the previous study’s findings.
Some additional reasons you might consider a career in data science include:
The wide variety of work environments
Data science usage has expanded significantly in recent years. A 2017 study concluded that the top three industries for data science and analytics were:
- Finance and insurance
- Professional services
- Information technology
Together, these sectors accounted for 59% of the job demand for the field.
Leading companies in industries ranging from marketing to logistics, and from health care to retail, are all on board. Each of these industries is looking for opportunities to leverage the insights generated by data scientists.
Expanding job opportunities
As companies begin to realize how important data analysis is for their continued prosperity, they’re regularly announcing new opportunities for data scientists. In fact, “data scientist” was named 2019’s most promising job by LinkedIn following a 56% rise in job openings compared to the previous year.
High income potential
The median salary for data scientists is $113,000. Those at the beginning of their career might expect to earn a base salary of $104,000. But after nine years, a salary in the range of $123,000 is considered the norm.
The importance of a hybrid skill set
Data scientists need a combination of soft skills and technical expertise to deliver the kind of efficiency and output that top companies are looking for. Candidates who possess in-demand hard skills, with a particular emphasis on Python or R, are expected to be the most sought after.
Digitization and its impact
As a data scientist, one of the most important growth skills you can develop is machine learning, which is expected to experience a 104% projected posting growth by 2023.
Industries with the highest levels of employment for data scientists and mathematical scientists, according to the U.S. Bureau of Labor Statistics (BLS), are:
- Computer systems design and related services
- Management of companies and enterprises
- Management, scientific, and technical consulting services
- Scientific research and development services
- Colleges, universities, and technical schools
Additionally, industries that employ a comparatively high proportion of data scientists and mathematical scientists compared to the rest of their staff include:
- Pharmaceutical and medicine manufacturing
- Cable and other subscription program services
The U.S. cities with the highest numbers of jobs for data scientists and mathematical scientists include some of the most significant hubs for technology and government.
Those areas are:
- New York City
- Chicago
- San Francisco
- Washington, D.C.
- Seattle
- Sacramento
- Los Angeles
- Atlanta
- Baltimore
- Phoenix
Sacramento, Baltimore, and San Francisco have the highest concentrations of people employed in the field, followed by these cities:
- Durham, North Carolina
- Raleigh, North Carolina
- Lansing, Michigan
- Dayton, Ohio
- San Jose, California
- Hartford, Connecticut
- Portsmouth, New Hampshire
The top-paying cities are San Francisco, San Jose, and New York City.
Entry-level opportunities
According to PayScale, entry-level data scientists received an average total annual compensation of $85,711. At the same time, data scientists in San Francisco, at all levels of experience, reportedly earned 27% more than the nationwide average. For more prominent companies, it’s not uncommon for entry-level data scientists to earn a starting salary above $100,000. This is especially true for industries that offer higher salaries overall, including some businesses in the technology space.
Advanced job options
Collaboration with your colleagues and the opportunity to work on specific projects may allow you to specialize or advance as a manager of data staff members, teams, or entire departments within an organization.
Average salaries according to PayScale for advanced, specialized occupations related to data science:
- Machine learning engineer: $111,804
- Application architect: $113,231
Average salaries according to PayScale for advanced data science roles:
- Senior data scientist: $125,809
- Chief data officer: $182,325
Computer systems design
Data scientists play a crucial role in developing custom solutions for clients who have specific data science needs. Depending on the client’s level of sophistication, you might have to take an outcome-oriented approach and work backward to help them develop data management solutions.
In computer systems design, you might also be responsible for creating more standardized product offerings, installing them, and consulting with businesses as they attempt to implement machine learning, automation, or data visualization products.
Business management
Data scientists may work for enterprises that manage a portfolio or network of constituent organizations. In this capacity, you might be responsible for cleaning and reporting internal data compiled by those constituent organizations for the benefit of the management enterprise. Analyzing this data may help the parent organization make informed decisions moving forward. Or you may be tasked with serving as an enterprise-sanctioned resource for the constituent organizations.
Consulting services
Consulting services could have several different uses for data scientists. If you worked for a consulting company, one role would entail in-house data science work to guide the group’s decision-making. However, you could also engage in research for client-facing staff members to help them develop data-driven recommendations. You might also work directly with the client on their data science strategy or review their data to provide your own professional insight.
In addition to knowing the right software and programming skills, you need to have highly developed soft skills, like the ability to regularly translate complex concepts into simple, straightforward language.
The most sought-after skills in data science
In addition to data science, these are the skills that you’ll need to possess for job openings between now and 2023:
Top Skill |
Projected Posting Growth (2018-2023) |
|---|---|
| Data Science | 133% |
| Machine Learning | 104% |
| Tableau | 95% |
| Data Visualization | 91% |
| Python | 89% |
| Process Design | 50% |
| R | 49% |
| Scrum | 47% |
| Spreadsheets | 46% |
Source: Burning Glass Technologies
Technical skills
Here are the specific technical skills that you’ll need to know, along with the percentage of job postings that have indicated a need for these abilities:
- SQL: 38%
- Microsoft Excel: 35.2%
- Python: 31.7%
- SAS: 30.8%
- R: 29.6%
- Tableau: 23%
- Microsoft PowerPoint: 15.5%
- Microsoft Office: 13.7%
- Data visualization: 10.2%
- Apache Hadoop: 8.8%
Soft skills in data science
These are the most in-demand soft skills that you’ll need to develop to apply for jobs as a data scientist. We’ve included the percentage of job postings that indicated each skill was required:
- Communication: 49%
- Teamwork and collaboration: 37.4%
- Problem-solving: 28.6%
- Research: 26.1%
- Creativity: 22.4%
- Attention to detail: 22%
- Planning: 19.4%
- Writing: 17.3%
- Organization: 16.8%
Top emerging skills
Data scientists with these specialized skills will be in increasingly high demand between now and 2023:
Emerging Skill |
Projected Posting Growth (2018–2023) |
|---|---|
| Deep Learning | 238% |
| Microservice | 229% |
| Data Lakes and Reservoirs | 201% |
| Golang | 200% |
| Qlik | 189% |
| Cloud Foundry | 182% |
| Alteryx | 170% |
| Adobe Analytics | 166% |
| Amazon Redshift | 137% |
Source: Burning Glass Technologies
Participating in an internship is a key way to enter the industry or change careers. A data science internship from one of these companies could help you get your foot in the door:
In addition to connecting with other professionals, membership in relevant industry groups means access to the latest information in the field. Consider joining one of these groups if you’re interested in growing your knowledge base and learning about new career opportunities:
A career field with many paths
With highly specialized and high-demand skills, you’ll have the opportunity to be a leader in the big data environment. As a data scientist, you’ll turn raw numbers into actionable insights and help businesses make a bigger impact.
Find your path
Sources
- Pearson — Consumer Report: Master of Data Science
- McKinsey — Catch them if you can: How leaders in data and analytics have pulled ahead
- Burning Glass Technologies — The Quant Crunch
- LinkedIn — The most promising jobs of 2019
- U.S. Bureau of Labor Statistics — Occupational Employment and Wages, May 2019: 15-2098 Data Scientists and Mathematical Science Occupations
- PayScale
- Fetch Rewards
- Redko
- TransUnion
- NBC Universal
- Data Science Association
- Association of Data Scientists
- American Statistical Association
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