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Analytics Magazine

Career Advancement: Where the analytics jobs are

March/April 2014

By Scott Nestler, CAPScott Nestler

“Because that’s where the money is.”
— Willie Sutton
(on why he robbed banks)

Consider the Obvious

In pondering an upcoming change of career (or at least employer), a good start is to follow Sutton’s Law, paraphrased in the apocryphal quote above and taught to medical students learning about diagnosis. In a more general form, it states something like, “first consider the obvious.” With regard to where jobs in analytics can be found, the obvious might include considering a mix of large, metropolitan cities and smaller, but technology-centric areas. While this proves to be a good approach, additional research yields results that are interesting, informative and potentially useful to anyone looking for a job in the analytics field.

The Realm of Possibilities

One line of investigation is to use LinkedIn to see where jobs related to analytics can be found. While there are many other job boards available (e.g., general sites like Monster, SimplyHired and Indeed, as well as more focused options like the INFORMS Career Center), this study used LinkedIn as a proxy for the universe of analytics job postings. Using the job search capability, it is possible to do a keyword search for all currently listed positions within a distance of a zip code. While poking around using the Sutton’s Law approach might be a useful start, a more systematic approach seems appropriate.

The idea of a metropolitan area seemed to be a good place to start, but what does that include (or leave out)? The U.S. government’s Office of Management and Budget (OMB) defines a number of statistical areas that might provide a useful framework. There are 388 metropolitan statistical areas (MSAs) with population greater than 50,000, and 541 micropolitan statistical areas (mSAs), with population between 10,000 and 50,000. There is also a grouping of adjacent MSAs and mSAs based on social and economic ties and incorporate commuting patterns; these 169 combined statistical areas (CSAs) seemed a good place to start, but initial exploration revealed that this list does not include MSAs that have only one urban core and therefore omits places like San Diego, Calif., Phoenix, Ariz., Tampa, Fla., and San Antonio, Texas. As these locations may be of interest to jobseekers in the analytics field, another approach is warranted.

Further searching revealed a list of 574 (unofficial but commonly used) groupings called primary statistical areas (PSAs), which include all 169 CSAs, 122 (of the 388) MSAs, and 283 (of the 541 mSAs). As this assemblage seems to have been developed for studies like this one, the 569 PSAs in the United States (but not Puerto Rico) were considered in this analysis.

Let’s Collect Some Data

While LinkedIn provides a straightforward search capability (for people, groups and jobs), there is also an advanced search capability. Exploring the advanced query indicates that a customized query can be produced by editing the uniform resource locator (URL) entered into a Web browser. The simplest representation of a query for searching within 25 miles (a reasonable commuting distance that incorporates a city and surrounding suburbs without overlapping nearby areas) of Washington, D.C., (zip code 20005) looks like this:
http://www.linkedin.com/vsearch/j?keywords=analytics&postalCode=20005&countryCode=us&distance=25

While the standard user interface only allows limited selections (e.g., distances of five, 10, 25, 50 and 100 miles), it is possible to customize the search (e.g., a distance of 12 miles) if desired. Using a few dozen lines of Python code, this can be automated to repeat an identical query (modifying only the zip code) for all 569 PSAs. The script took about an hour to run, due to the addition of some random delays between queries to emulate human behavior and possible LinkedIn account suspension. While the actual number of job listings changes from one day to the next, and even during the course of a day, the results included here are likely representative of the relative job markets applicable to those looking for jobs in analytics.

Bigger is Generally Better

Table 1: Analytics jobs in the 10 largest U.S. metropolitan areas.

On Feb. 1, 2014, there were a total of 11,584 jobs containing the keyword “analytics” on LinkedIn. A total of 339 of the 569 PSAs had no analytics job listings on this day. Quants looking for work can probably skip Fresno, Calif.; Vernon, Texas; and a few hundred other locations. As might be expected, larger cities in general have more analytics jobs than smaller towns. Table 1 shows the 10 largest cities, their population (in millions) and the numbers of analytics job listings. The correlation between population and jobs is quite high (0.85), but even a cursory look at Table 1 shows that some large cities (e.g., Chicago and Miami) might not be “pulling their weight” in terms of providing jobs in analytics.

Bigger Isn’t Always Better

Table 2 shows the 20 PSAs with the greatest number of jobs. Not surprisingly, nine of the 10 largest metropolitan areas (indicated by grayed-out text) are also included in this list. The “Top 10” accounted for 7,274 (or 73 percent of all) jobs, while the “Top 20” covered 6,264 (or 80 percent of all) jobs. Not surprisingly, the distribution of analytics jobs is skewed toward a small number of locations. Figure 1 shows a Pareto diagram of the first 50 (sorted by number of jobs) PSAs. Note that these locations account for 94 percent of all listed positions.

Table 2: 20 metropolitan areas with the most analytics jobs.

We compute a “persons per job” metric in an attempt to determine which areas are “punching above their weight,” and identify smaller areas that have an unusually high (relative to their total population) number of analytics jobs. This is computed by dividing the total population by the number of analytics job listings within the CSA. As shown in Table 3, Platteville, Wis., leads in this category with roughly one analytics job opening for every 800 people; keep in mind that this includes people of all ages, including children too young to work and retirees, not just those eligible for work. This compares to an overall average (for those areas with at least one advertised position) of one opening per 122,000 people. So, some areas are a more “target rich environment” than others for job hunters. While looking at some of these smaller areas that provide more “bang for the buck” (in terms of the number of analytics jobs relative to the population) may seem wise, some caution may be in order. Taking one of the three available positions in Lewistown, Pa., might be appealing to someone who prefers a more rural setting, but if the situation didn’t work out, there might not be any other opportunities in the area, potentially necessitating an unexpected relocation.

Figure 1: Pareto diagram of jobs in 50 PSAs. Figure 2: Info graphic view of analytics jobs. Circles are scaled to represent the number of jobs in each location.

 

 

 

 

 

 

A Picture Is Worth 11,584 Jobs

Table 3: 10 Micropolitan (and metropolitan) areas with greatest “jobs per capita.”

Figure 2 shows an info graphic view of the 230 areas with at least one analytics job. The circles are scaled to represent the number of jobs in each location; however, the largest circle (New York) is only 200 times larger than the smallest, not 2,000 times larger if it were true to scale. As you can see, they are spread out across the U.S., with opportunities in many geographic regions and climatic zones. All in all, it appears to be a good job market for those with skills in analytics. However, the majority of the available positions appear to exist in a relatively small number of metropolitan and micropolitan areas. After all, 90 percent of the listed jobs are in only 35 locations. Happy job hunting to those who are looking!


Scott Nestler, Ph.D., CAP®, PStat®, is an Army operations research analyst, a member of INFORMS and chair of the INFORMS Analytics Certification Board (ACB).

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of the Army, the Department of Defense or the U.S. Government.

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