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Data scientists solve problems. They possess the technical savvy to unravel complex queries and the creativity to know how to get there. Often seen as part mathematician, part computer scientist – and all trailblazer – data scientists work to gain insights, and ultimately find purpose in petabytes worth of unorganized, scattered and often disparate data.

While the private sector and governments alike accumulate untold quantities of data, and as big data storage and processing technologies have become more capable while also becoming cheaper with every passing year, data science has seen a serious evolution.

Now big data is no longer a hassle for IT to handle—it’s a virtual gold mine of information, just waiting for data scientists to translate into innovative ideas that have implications for commercial and even social change.

In its simplest terms, data science is about obtaining, organizing, and manipulating data to gain insights. It is also about communicating those insights to strategists and decision makers who can then use the newly unearthed information to take action.

Data scientists possess a deep understanding of the organizations and industries they support and know which questions to ask; questions that involve looking into the invisible relationship between disparate data sets. In many cases, these are questions most wouldn’t even think to ask or wouldn’t believe were possible to answer. However, the insights the answers uncover have been known to be worth billions of dollars in savings and new revenue streams.

How Data Scientists Fit in to the Big Picture … In Virtually Any Business or Government Agency

The value of data scientists to a business or public sector organization lie with their ability to understand what is important, how their organization can benefit, and how to best communicate their findings to decision makers within the organization.

Data scientists are valuable members of the IT team, for a multitude of reasons:

  • Data scientists serve as trusted advisors and strategic partners, ensuring that their organization maximizes their analytics capabilities.
  • Data scientists communicate and demonstrate the value of analytics to decision makers within an organization, thereby facilitating the decision-making process.
  • Data scientists address key business challenges by extracting insight from data and driving action.
  • Data scientists strive to constantly improve the value of an organization’s analytics system by questioning existing processes and developing new methods and analytical algorithms.
  • Data scientists understand how the implementation of new strategies, processes and behavioral changes based on data science can affect an organization’s bottom line.

The Processes, Proficiencies and Goals of Data Science

A data scientist’s typical job duties include:

  • Developing predictive systems and creating efficient algorithms to improve data quality
  • Identifying, evaluating, designing, and implementing statistical analyses of gathered data to create analytic metrics and tools
  • Designing, building, and deploying data analysis systems for large data sets
  • Creating algorithms to extract information from large data sets
  • Establishing efficient, automated processes for model development, validation, implementation, and large-scale data analysis
  • Developing metrics and prototypes used to drive business decisions
  • Identifying emergent trends and opportunities for future client growth and development

Their work includes employing a number processes, including:

  • Data visualization: Presenting data in a visual format (picture, graphic format) so it can be easily analyzed
  • Machine learning: Mathematical algorithms and automation (includes deep learning, which uses data to model complex abstractions)
  • Pattern recognition: Technologies that recognize patterns in data (often used with—or as an alternative to—machine learning)
  • Data preparation: Converting raw data into another format so it can be easily consumed
  • Text analytics: Examining unstructured data to glean key business insights

Just a few of the proficiencies expected of data scientists include:

  • Statistics, machine learning
  • Coding languages (R, Python, SAS, etc.)
  • Databases (Postgres, MySQL, etc.)
  • Big data storage tools (Hadoop, Greenplum, MapReduce, etc.)

Data scientists possess the unique ability to analyze massive data sets generated by web logs, sensor systems, and transactional data as to glean insights otherwise overlooked in the mess of near-endless amounts of scattered data.

They are nearly twice as likely to use big data storage tools as other data professionals, and they frequently work in teams with statisticians, programmers, IT administrators, and other data scientists, all of whom combine efforts to gather, organize, and put big data into action.

The Skills and Credentials Employers Look for When Hiring Data Scientists

Data scientist job descriptions often vary according to the employer’s needs, including their chosen software technologies and statistical analysis tools. However, a typical job description for a data scientist likely outlines the following requirements:

  • An advanced degree (Masters or PhD) in a relevant field:
    • Computer science
    • Data science
    • Statistics
    • Applied Math
    • Physics
  • A strong background in statistical concepts and calculations, infrastructure design, cloud computing, and data warehousing
  • Proficiency with statistical analysis tools to include:
    • R
    • SAS
    • SPSS
  • Proficiency with software development technologies to include:
    • Python
    • C++
    • Java
  • Experience with big data tools to include:
    • Hadoop
    • Cassandra
    • Storm
  • Excellent critical thinking skills
  • Excellent verbal and written communication skills
  • Excellent leadership skills
  • Ability to:
    • Work in a fast-paced environment
    • Promptly recognize emerging problems and identify potential solutions
    • Deliver high-quality results on time

A 2015 study by Burtch Works, an executive recruiting firm, revealed the traits common among the data scientists they surveyed:

  • Data scientists are young, possessing an average of six years of experience.
  • Data scientists are highly educated—92 percent have at least a master’s degree.
  • Nearly one-third (29 percent) of data scientists possess a degree in mathematics or statistics, while about 18 percent hold a degree in computer science.
  • More than one-third (36 percent) of data scientists are employed on the West Coast.
  • About 43 percent of data scientists work for organizations in the technology and gaming industries.
  • The median, annual salary of data scientists ranges from $91,000 for those with one to three years of experience and up to $250,000 for managers leading a team of 10 or more.

Top Employment Sectors and Employers

Today’s data scientists work in nearly all sectors, industries, organizations, and businesses throughout the world. Their work results in everything from increased revenue for commercial enterprises to improvements in medicine that save lives. Just a few of the areas where data science has begun to deliver significant improvements include:

  • National security
  • Business intelligence
  • Law enforcement
  • Financial analysis
  • Healthcare
  • Disaster preparedness
  • Finance and insurance
  • Biotechnology
  • Defense and counterterrorism
  • Genomics
  • Agriculture
  • Materials science
  • Pharmaceutical R&D
  • Marketing and online commerce
  • Energy production
  • Environmental science
  • Technology and gaming

For example, in commercial organizations, data scientists use big data by determining its value, analyzing it, and building algorithms for the express purpose of improving how they do business-  and their bottom line. And in the healthcare industry, data scientists use big data to predict health trends, map genes, and provide personalized treatment plans and even customized pharmaceutical compounds.

Many data scientists also work directly for data science companies. A 2015 Forbes article ranked the best data science companies based on whether employees would recommend the company to a friend. The following companies ranked among the most popular:

  • InsightSquared
  • Paxata
  • Trifacta
  • Cloudera
  • Birst
  • Sumo Logic
  • Gainsight
  • Google
  • Ayasdi
  • Visier

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