Emerging Jobs: Predictive Analytics Professionals in Demand

Posted By Terri Williams on November 8, 2016 at 8:12 am
Emerging Jobs: Predictive Analytics Professionals in Demand

Big data means big business these days, and companies clamor for people who can translate data into insight and solutions. Predictive analytics, business intelligence, and data science are among the professions in high demand, and some companies even lament the fact that finance and accounting majors lack big data analysis skills.

Remember when a megabyte was considered a lot of data? Then came gigabytes, terabytes, petabytes, and exabytes. Now zettabytes and yottabytes are needed to keep up with all the available information. But this data is useless without people to harness it.

While data scientists remain the darlings of the big data movement, predictive analytics professionals, also know as PAPs, are not far behind. The Burtch Works Study: Salaries of Predictive Analytics Professionals, September 2016, provides insight about this emerging career. Below are selected excerpts:

Compensation of individual contributors by job level:

Job Level Typical years of experience Median Base Salary Median Bonus
Individual Contributor, Level 1 0-3 years $75,000 $7,150
Individual Contributor, Level 2 4-8 years $95,000 $10,545
Individual Contributor, Level 3 9 years $126,500 $17,775


Compensation of managers by job level:

Job Level Typical # of reports Median Base Salary Median Bonus
Manager, Level 1 1-3 reports $130,000 $20,188
Manager, Level 2 4-9 reports $175,000 $38,000
Manager, Level 3 10+ reports $225,000 $62,100


Distribution of predictive analytics professionals by education:

69% Master’s degree
17% Ph.D.
13% Bachelor’s


Predictive analytics differs from data science or business intelligence

Now that we’ve answered the most popular questions (how much does it pay and what degree is required), let’s back up to answer the most important question: What is predictive analytics? Katie McConkey, assistant professor of industrial and systems engineering at the Rochester Institute of Technology, tells GoodCall it is the science of learning from the past to predict the future. “Predictive analytics can be considered a subset of data science focusing specifically on predicting future values and events,” McConkey says. “Data science can more broadly be split into descriptive, predictive, and prescriptive analytics.” She provides examples in a cybersecurity setting:

  • Descriptive analytics allow us to understand correlations between past events and past cyber attacks.
  • Predictive analytics would be the use of those event correlations to predict new cyber attacks in the future before they happen.
  • Prescriptive analytics goes beyond predictive analytics by suggesting optimal courses of action to take based on the present situation and future forecast, for example, a prescriptive analytic could suggest migrating the operating system of a server to minimize risk of exposure.

Applications of predictive analytics

Predictive analytics is in high demand because it has so many applications. In addition to homeland security, McConkey says it can be used in healthcare, energy, and more. “As computing systems and data become more abundant, predictive analytics is a natural progression from just analyzing what the data means to what one can anticipate or project into the future,” McConkey says.

It’s also quite popular in marketing, according to Sandy Marmitt, partner & senior executive recruiter at Burtch Works. “It enables a company to better evaluate a customer’s behavior and how they will react to certain marketing tactics or what their next steps might be,” Marmitt says. “For example, one customer might be more likely to purchase a product if sent an email coupon, whereas another might be more likely to purchase if they see an ad for a sale.”

Predicting the future in any shape, form, or fashion is invaluable to companies. Shanchieh (Jay) Yang, professor and the department head of the computer engineering department at Rochester Institute of Technology, tells GoodCall, “If we can predict consumer behaviors, we can anticipate future product demands, providing inventory at just the right levels.”

But the ability to anticipate future behaviors isn’t just limited to leveraging sales. “If we can predict cyber attacks or adversary behaviors, we can work to mitigate the risk; if we can predict hospital arrival rates or service needs, we can staff hospitals at the most efficient level,” Yang says.

Yang says predictive analytics also can be used to maximize on future trends. “If we can anticipate the demand for electrical energy with the increasing number of electrical vehicles on the road and the supply of renewable energy, we can better plan the smart grid infrastructure.”

Educational requirements for predictive analytics jobs

While most of these professionals have an advanced degree, it might not be in predictive analytics since this is such a young profession. Marmitt tells GoodCall that degrees are usually in such areas as math, economics, statistics, or operations research. “Sometimes MBA programs might have a quantitative focus, and a lot of business schools have been creating new programs to meet the growing need, but an MBA is usually not quantitative enough by itself without already having a strong statistics or math background.”

However, Marmitt says there has been an increase in bootcamps and massive open online courses, or MOOCs. “But the most successful individuals from those programs tend to already have a quantitative background and are using the MOOCs or bootcamp to shore up on individual skills, not to learn everything from the ground up,” Marmitt explains.

Terri Williams
Terri Williams graduated with a B.A. in English from the University of Alabama at Birmingham. Her education, career, and business articles have been featured on Yahoo! Education, U.S. News & World Report, The Houston Chronicle, and in the print edition of USA Today Special Edition. Terri is also a contributing author to "A Practical Guide to Digital Journalism Ethics," a book published by the Center for Digital Ethics and Policy at Loyola University Chicago.

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