burtch big data
TRANSCRIPT
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The
Study
Salaries for Big Data ProfessionalsJuly 2013
Burtch Works Executive RecruitingLinda Burtch, Managing Director
BurtchWor s
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TABLE OF CONTENTS
Section 1: Introduction ......................................................................................... 3
Study Objective ................................................................................................................... 4
About Burtch Works ........................................................................................................... 4
Why the Burtch Works Study is Unprecedented ................................................................ 5
Section 2: Study Design ......................................................................................... 6
The Sample ......................................................................................................................... 7
Identifying Big Data Professionals ...................................................................................... 7
Completeness and Age of Data ........................................................................................... 8
Segmentations of Big Data Professionals ........................................................................... 9
Section 3: Big Data Professionals: Who They Are ................................................. 12
Overview ........................................................................................................................... 13
Age of Big Data Professionals ........................................................................................... 14
Gender of Big Data Professionals ..................................................................................... 15Education of Big Data Professionals ................................................................................. 16
Residency Status of Big Data Professionals ...................................................................... 17
Section 4: Big Data Professionals: What They Earn .............................................. 19
Overview ........................................................................................................................... 20
Compensation by Job Level .............................................................................................. 21
Compensation by Education ............................................................................................. 23
Special Report: Compensation for Entry Level Jobs by Education .................................... 26
Compensation by Residency Status .................................................................................. 27
Compensation by Region .................................................................................................. 29
Compensation by Industry ................................................................................................ 32
Compensation by Gender ................................................................................................. 37
Section 5: Special Report: Salary Increases of Big Data Professionals ................... 38
Overview ........................................................................................................................... 39
Base Salary Change by Job Title ........................................................................................ 40
Base Salary Change by Education ..................................................................................... 41
Section 6: Where do we go from here? ................................................................ 42
Recruiting & Retaining Big Data Professionals.................................................................. 43
Section 7: Appendix ............................................................................................ 44Glossary of Terms ............................................................................................................ 45
Burtch Works Executive Recruiting, 1560 Sherman Avenue, Suite 1005, Evanston, IL 60201
847-440-8555 | www.burtchworks.com | [email protected]
Survey design consulting services provided by:
Fred Crandall, Ph.D., Managing Director, Eastwood Group Partners, Ltd.
© 2013, Burtch Works LLC. Unauthorized reproduction is strictly prohibited. Opinions reflect judgment at time of
publication and are subject to change.
http://www.burtchworks.com/http://www.burtchworks.com/http://www.burtchworks.com/mailto:[email protected]:[email protected]:[email protected]:[email protected]://www.burtchworks.com/
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Section 1
INTRODUCTION
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Study Objective
The purpose of this report is to provide up-to-date ifoatio o the opesatio of Big Data
professionals. Big Data pofessioals ae idiiduals ho a appl sophistiated uatitatie skills
to data describing transactions, interactions or other behaviors of people to derive insights and
pesie atios. The ae distiguished fo the uats of the past the shee uatit of
data on which they operate, an abundance made possible by new opportunities for measuring
behaviors (most notably, the opportunity to measure what people do online, but there are many
others, such as those presented by customer loyalty programs) and advances in technologies for the
storage and retrieval of data (for example, Hadoop).
Because the value of Big Data has been demonstrated many times, the demand for Big Data
professionals is growing rapidly. Despite a keen need for current and reliable information about the
compensation of Big Data professionals, no such information has been available. Burtch Works
here takes advantage of data that it has for 2,845 Big Data professionals in the U.S. to report who
they are, how much they earn, and how their pay varies with level of responsibility, years of
experience, education, where they live, and the industry in which they work.
About Burtch Works
Burtch Works Executive Recruiting is a team of recruiters with decades of experience placing
quantitative professionals in the most in-demand jobs on the market. They have long-established
relationships with thousands of professionals who work in various industries to support the recent
influx of information, as well as with hundreds of companies that rely on workers to harness the
power of marketing analytics. Burtch Works is therefore able to provide unparalleled insight into
the hiring and compensation of these professionals and with this report further shed light on a
previously unexplored area of the job market.
Linda Burtch, the Managing Director of Burtch Works, has 30 years of experience recruiting inanalytics and knows the space and the talent thoroughly. She has maintained a blog for many
years, writing on topics of importance to the analytics community. In addition, she maintains a
strong social network presence serving as a conduit for conveying relevant information and follows
influential leaders in the analytics community closely. She has also been a frequent speaker on Big
Data career topics at luncheons, conferences, corporate gatherings and webinars. Linda has been
part of the Executive Board of the Chicago Chapter of the American Statistical Association and is
uetl seig as the Boad’s Pesidet.
Ms. Burtch and her colleagues have strong relationships with over 17,000 quantitative
professionals, many of whom Burtch Works has kept in close touch with throughout their career
statig ith the opletio of thei Maste’s ad Ph.D. pogas. As these idiiduals pogess itheir careers, Burtch Works maintains regular correspondences through phone calls, emails, and
networking events. Through these interactions, Burtch Works has meticulously collected invaluable
ifoatio aout pofessioals’ opesatio ad the fatos that ifluee it suh as thei
education, years of experience and industry of employment.
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The Big Data professionals in this study have worked at more than 700 firms that include large
multinational corporations, start-ups, management consulting firms, advertising agencies, niche
quantitative firms, and research organizations. Burtch Works communicates with the hiring
managers and human resources specialists of such companies who are staffing quantitative roles on
a daily basis, staying abreast of job requirements and hiring practices. This latest report is a
culmination of their findings which they are excited to present to both employers and employees in
the far reaching world of Big Data.
Why the Burtch Works Study is Unprecedented
The Burtch Works Study is different from any other compensation survey because:
This study focuses solely on the compensation of Big Data professionals, and the results
are not confounded with trends in the compensation of other related professionals, such as
web and business intelligence analysts. Burtch Works assiduously excluded data about the
compensation of these other professionals from the data used to derive the results
reported here. Although Burtch Works has relationships with far more quantitative
professionals, compensation data for only 2,845 judged to be Big Data professionals withdeep analytical skills were used for this study.
Burtch Works staff collected the data by interviewing Big Data professionals about their
current jobs. This approach differs from the traditional approach used for salary surveys,
which is to obtain compensation data from human resources departments, who have
difficulty identifying those employees of their firms who fall into this category. This is
eause a fi’s Big Data pofessioals ae tpiall ot i a depatet that is ol fo suh
professionals and, instead, are found in many departments throughout a firm. Moreover,
job titles of Big Data professionals, which vary widely across and even within firms, often do
not make clear that they are Big Data professionals. Another important advantage of the
interview process is that Burtch Works’ staff was able to obtain information about theprofessionals not often provided by human resources departments but with which
compensation often varies, such as education and residency status. Finally, because of their
knowledge of the Big Data profession, when recruiters conducted interviews, they were
able to obtain corrections or clarifications when information provided by the professionals
did not seem credible.
Burtch Works shows how compensation varies by region, industry, education and
residency status. Burtch works developed a categorization of jobs by management
responsibility (whether the job includes a responsibility for managing other employees) and
level (level of management responsibility or depth of expertise) and then assigned each
individual for which it has compensation data to one of these categories. This is typicallydone for compensation surveys. However, because Burtch Works has compensation data
for 2,845 Big Data professionals, there were many individuals assigned to most of these
categories. Consequently, in addition to showing how compensation varies across these
categories, Burtch Works also shows how compensation varies within a category by region,
industry, education and residency status.
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Section 2
STUDY DESIGN
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The Sample
The sample consists of 2,845 of the more than 17,000 quantitative professionals with whom Burtch
Works has relationships. All of these 2,845 professionals have the education, skills and job
responsibilities typical of Big Data professionals. Additionally, for each one, Burtch Works has the
data necessary to show how Big Data professionals are compensated and what their compensation
depends on. Finally, for each professional in the sample, the data available were collected in aninterview done no more than 30 months ago.
Identifying Big Data Professionals
Burtch Works looked at the education, skills and job responsibilities of quantitative professionals to
identify those who are Big Data Professionals.
Firstly, Big Data professionals have a degree – usuall a adaed oe, suh as a Maste’s degee
or Ph.D. – in a quantitative discipline such as Statistics, Applied Mathematics, Operations Research
or Economics. In addition, some professionals with an MBA were also judged to be Big Dataprofessionals if they described their MBA program as one having a quantitative emphasis, which is
often true of graduates of business schools such as those at the Massachusetts Institute of
Technology and Carnegie Mellon University.
Secondly, Big Data professionals generally have skill using one or more tools for operating on Big
Data, such as SAS, R, Hadoop, and SQL.
Thirdly, Burtch Works looked for Big Data professionals with job responsibilities in one of these
areas:
Analytical Database Marketing: Studies existing customers with using methods such ascustomer segmentation, campaign targeting and effectiveness, propensity modeling, and
customer lifetime value analysis.
Analytics Management: Manages analytics projects, usually without being hands-on with
data (might use Excel, but no advanced tools). Sometimes does not have a formal education
in one of the quantitative disciplines in the list above.
Business Intelligence: Specializes in establishing data warehouses and other infrastructure
for accommodating Big Data, and might also have a responsibility for basic analytics and
reporting.
Credit Risk Analytics: Measures consumer, enterprise, and market risk levels. Results of
analyses might impact the price of product, such as the interest rate for a credit card or its
availability, as in the case of a loan.
Data Science: Utilizes proficiency for data management and analytical skills, to make Big
Data accessible and derive useful information from Big Data.
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Marketing Science: Predicts consumer behavior using analytics such as marketing mix
modeling. Analysis can use transaction-, store-, or market-level data.
Operations Research: Finds optimal solutions to problems such as those that often occur in
logistics, manufacturing, inventory management, and revenue yield management using
methods such as linear, integer and network programming.
After consideration of the work done by individuals in Analytics Management, Business Intelligence,and Operations Research jobs, Burtch Works decided to exclude them from the sample. Data
scientists, however, are certainly Big Data professionals, but because this subset of professionals is
so new, and because their compensation is so unlike that of other Big Data Professionals, Burtch
Works decided to exclude them from the sample and will, in the future, publish a separate study for
data scientists.
Completeness and Age of Data
Burtch Works included a professional in the sample only if it has complete data for the professional.
This includes compensation data – base salary, bonus eligibility and last bonus received -- but alsoknowledge of whether a professional manages other people and the number managed, years of
experience, region of the U.S. where the professional lives, industry of employment, education,
residency status, and gender. Burtch Works required all of these data so that it can describe the
current population of Big Data professionals and show how their compensation varies with their
attributes.
Additionally, Burtch Works included a professional in the sample only if the data available for the
professional was obtained in the last 30 months, which is typical of compensation surveys. Each of
the 2,845 professionals in the sample was interviewed by a Burtch Works recruiter at some point
during the 30 months ending May, 2013, most within the last year. Recruiters did these interviews
in the course of executing searches for clients.
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Segmentations of Big Data Professionals
Burtch Works segmented Big Data professionals to investigate how their compensation varies with
demographic attributes of the professionals and characteristics of their jobs. Some of these
segmentations are straightforward, such as segmentations by education, region, industry of
employment, and residency status. However, Burtch Works also divided the professionals into six
categories based on whether a professional manages employees and, if so, the level ofmanagement responsibility, and if not, the depth of expertise:
Individual Contributors
Level ResponsibilityTypical Years
of Experience
Level 1 Learning the job, hands-on analytics and
modeling
0-3 years
Level 2 Hands-on with data, working with more
advanced problems and models, may
help train Analysts
4-8 years
Level 3 Considered an analytics Subject Matter
Expert, mentors and trains analysts
9+ years
Managers
Level ResponsibilityTypical Number
of Reports
Level 1 Tactical manager who leads a smallgroup within a function, responsible for
executing limited projects or tasks within
a project
1-3 reports(direct or matrix)
Level 2 Manager who leads a function and
manages a moderately sized team,
responsible for executing strategy
4-9 reports
(direct or matrix)
Level 3 Member of senior management who
determines strategy and leads large
teams, manages at the executive level
10+ reports
(direct or matrix)
Figure 1. Definition of Individual Contributor Job Levels
Figure 2. Definition of Manager Job Levels
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Burtch Works divided the U.S. into these five categories:
Northeast
Southeast
Midwest
Mountain
West Coast
These regions are defined as Figure 3 shows below:
Figure 3. U.S. Geographic Regions
WEST
COAST
MOUNTAIN MIDWEST
SOUTHEAST
NORTHEAST
Note: The Northeast included areas of Virginia within 50 miles of Washington, DC, and the Midwest included areas ofPennsylvania within 75 miles of Pittsburgh.
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The firms for which professionals work were divided into these nine industries:
Advertising/Marketing Services
Consulting
Consumer Packaged Goods
Financial Services
Healthcare/Pharmaceuticals
Outsourcing
Retail
Tech/Telecom
Other
Each professional was assigned to one of these five residency status categories:
U.S. Citizen
F-1/OPT
H-1B
Permanent Resident Other
Finally, each professional was in one of these five education categories:
No college degree
Bahelo’s degee
Maste’s degee
Ph.D. all-but-dissertation (ABD)
Ph.D.
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Section 3
BIG DATA PROFESSIONALS:
WHO THEY ARE
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Overview
• Big Data professionals are young. Three quarters of them have no more than 15 years of
experience.
• Big Data professionals are overwhelmingly male, particularly those at more senior levels.
• Big Data pofessioals ae highl eduated. % hae at least a Maste’s degee.
• 39% of Big Data professionals are not U.S. citizens. Significantly fewer than half of individual
contributors at levels 1 and 2 are U.S. citizens.
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Age of Big Data Professionals
The recruiters at Burtch Works do not ask the age of the professionals with whom they work.
However, they do ask them for their years of work experience, which is highly correlated with age.
Big Data professionals are likely to be young: the median years of experience is eleven. Three
quarters of Big Data professionals have no more than 15 years of experience.
Figure 4. Big Data Professionals by Years of Experience
0
100
200
300
400
500
600
700
0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 40+
N u m b e r o f P r o f e s s i o n a l s
Years of Experience
Median: 11 years
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Gender of Big Data Professionals
As is true of many professions that require an education in a STEM field, there are relatively few
women among Big Data professionals: only 25% are women. The more years of experience a
professional has, the less likely it is that the professional is a woman:
These results are in line with other STEM findings:
Math, Science, and Engineering: e to ee oa i the field1
Computer Science: oe opise -% of the oputig okfoe2
Science, Technology, Engineering and Math: women occupy less than a quarter of the
STEM positions3
1 Women affected by male to female ratio in math, science and engineering settings. Association for Psychological Science.
http://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-
and-engineering-settings.html 2 Women in computing. http://en.wikipedia.org/wiki/Women_in_computing
3 The STEM gender gap. Decisions Based on Evidence. http://www.decisionsonevidence.com/2013/04/the-stem-gender-gap/
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
0-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
40+
Y e a r s
' E x p e r i e n c e
Male Female
Figure 5. Gender of Big Data Professionals by Years of Experience
http://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-and-engineering-settings.htmlhttp://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-and-engineering-settings.htmlhttp://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-and-engineering-settings.htmlhttp://en.wikipedia.org/wiki/Women_in_computinghttp://en.wikipedia.org/wiki/Women_in_computinghttp://en.wikipedia.org/wiki/Women_in_computinghttp://www.decisionsonevidence.com/2013/04/the-stem-gender-gap/http://www.decisionsonevidence.com/2013/04/the-stem-gender-gap/http://www.decisionsonevidence.com/2013/04/the-stem-gender-gap/http://www.decisionsonevidence.com/2013/04/the-stem-gender-gap/http://en.wikipedia.org/wiki/Women_in_computinghttp://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-and-engineering-settings.htmlhttp://www.psychologicalscience.org/index.php/news/releases/women-affected-by-male-to-female-ratio-in-math-science-and-engineering-settings.html
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Education of Big Data Professionals
Big Data professionals are also highly educated: 86% have an advanced degree.
Figure 6. Big Data Professionals by Education
Bachelor's
13%
Master's
64%
PhD, ABD
2%
PhD
20%
No Degree
1%
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Will not
transfer
27%
Will transfer67%
Will transfer
exceptionaltalent
6%
All Industries 22%
70%
8%
Consulting/Advertising
42%
50%
8%
Retail
23%
77%
Corporate (non-retail)
Because so many of the individuals with the best training and experience to be Big Data
professionals are from abroad, most companies employing these professionals are willing to
sponsor applications to obtain or transfer visas:
Figure 9. Firms Employing Big Data Professionals by Willingness to Sponsor Visa Applications or Transfers
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Section 4
BIG DATA PROFESSIONALS:
WHAT THEY EARN
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Overview
• Salaries and bonuses are greater for managers than for individual contributors, and they
increase significantly with level.
• Among individual contributors and the most junior managers, salaries vary with education
completed.
• Education particularly influences salaries of Big Data professionals hired for entry level jobs.
• Surprisingly, among individual contributors at levels 1 and 2, non-U.S. citizens are paid
higher salaries than U.S. citizens.
• Firms on the West Coast pay the highest salaries to individual contributors, while firms in
the Northeast pay the highest salaries to managers.
• Firms in the consulting industry pay high salaries to both individual contributors and
managers, while firms in the tech/telecom industry also pay high salaries to individualcontributors. Healthcare and pharmaceutical companies are also among those paying the
highest salaries to Big Data professionals.
• Salaries of Big Data professionals also vary with gender, but not nearly as much as the
salaries of practitioners of other professions. Across job levels, women in Big Data never
earn less than 90% of their male counterparts.
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Compensation by Job Level
Figure 10 shows the distribution of individual contributors and managers, overall median base
salary and the proportion eligible for a bonus. Figures 11 and 12 show median and mean base salary
of Big Data professionals by job level. For each job level, they also show the proportion eligible for
a bonus. For those who received a bonus, they show the median and mean values of the last bonus
received. Figures 13 and 14 show how much median base salary increases with level for individualcontributors and managers.
Not surprisingly, Big Data professionals who are managers make considerably more than those who
are individual contributors, and compensation also depends on job level.
• 58% of the professionals in the sample are individual contributors, and their median base
salary is $90,000. The median base salary of the 42% of the professionals who are
managers is $145,000.
• 66% of individual contributors are eligible for bonuses, and the median value of the last
bonus received is approximately $10,000. 83% of managers are eligible for bonuses, andthe median value of last bonus received is $29,250.
• For both individual contributors and managers, median base salary increases significantly
with level. In both cases, the median salary of professionals at level 3 is almost 80% higher
of those at level 1.
• For both individual contributors and managers, the proportion eligible for a bonus increases
in level, and the bonuses paid to those at level 3 are much greater than bonuses paid to
those at levels 1 or 2.
Managers
42%Individual
Contributors
58%
Median Base Salary
$145,000
Bonus Eligible83%
Median Base Salary
$90,000
Bonus Eligible66%
Figure 10. Distribution of Individual Contributors & Management
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Individual
Contributor
Job Level
Base SalaryBonus
Eligible
Actual Bonus
N 25% Median Mean 75% Median Mean
Level 1 386 $60,000 $65,000 $69,313 $80,000 54.9% $6,300 $7,783
Level 2 537 $70,500 $85,000 $84,908 $95,000 69.3% $8,840 $10,393Level 3 715 $95,000 $115,000 $117,647 $135,000 70.2% $15,425 $23,079
Manager
Job Level
Base Salary
Bonus
Eligible
Actual Bonus
N 25% Median Mean 75% Median Mean
Level 1 440 $104,000 $120,000 $119,466 $135,000 80.5% $18,000 $21,088
Level 2 592 $135,000 $151,500 $156,573 $175,000 83.8% $32,000 $38,012
Level 3 173 $190,000 $215,000 $230,318 $250,000 94.2% $62,750 $83,913
Figure 11. Compensation of Individual Contributors by Job Level
Figure 14. Median and Mean Base Salaries of
Managers by Job Level
$50,000
$70,000
$90,000
$110,000
$130,000
$150,000
$170,000
$190,000
$210,000
$230,000
$250,000
Level 1 Level 2 Level 3
Figure 13. Median and Mean Base Salaries of
Individual Contributors by Job Level
$50,000
$70,000
$90,000
$110,000
$130,000
$150,000
$170,000
$190,000
$210,000
$230,000
$250,000
Level 1 Level 2 Level 3
MeanMedian
Figure 12. Compensation of Managers by Job Level
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Compensation by Education
Figures 15 through 18 show the distribution of the base salaries of Big Data professionals by
education, controlling for job level.
Among individual contributors of all job levels, base salary varies significantly with
education. An individual contributor with a Ph.D. is typically paid at least $15,000 more thanoe ith ol a Bahelo’s degee ad at least $, oe tha o e ith a Maste’s
degree.
Among managers at job level 1, those with a Ph.D. also make significantly more than those
ith a Bahelo’s degee ad soehat oe tha those ith a Maste’s degee.
A Ph.D. o Maste’s degee does ot appea to ig a highe sala aog oe seio
managers, perhaps because good performance in their jobs depends at least as much on
management skills as on technical skills.
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Note: Individuals with no degrees were excluded because of the small sample size.
$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
$100,000
$110,000
$120,000
$130,000
Level 1 Level 2 Level 3
Bachelor's Master's PhD, ABD PhD
Job Level Education Base SalaryN 25% Median Mean 75%
Individual
Contributor,
Level 1
Bachelor's 48 $53,500 $59,500 $59,565 $65,000
Master's 255 $60,000 $65,000 $68,401 $75,000
PhD, ABD 2 - - - -PhD 46 $70,000 $81,000 $81,359 $90,000
Individual
Contributor,
Level 2
Bachelor's 70 $70,000 $76,000 $80,260 $93,000
Master's 340 $70,000 $82,000 $82,945 $93,000
PhD, ABD 10 $65,000 $84,500 $84,850 $100,000
PhD 78 $85,000 $95,000 $97,631 $110,000
Individual
Contributor,
Level 3
Bachelor's 93 $86,000 $108,000 $112,097 $130,000
Master's 426 $90,000 $110,000 $114,763 $130,000
PhD, ABD 17 $110,000 $130,000 $128,941 $141,000
PhD 120 $105,000 $122,950 $126,799 $140,000
Figure 15. Base Salary of Individual Contributors by Job Level and Education
Figure 16. Median Base Salary of Individual Contributors by Job Level and Education
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Note: Individuals with no degrees were excluded because of the small sample size.
$90,000
$110,000
$130,000
$150,000
$170,000
$190,000
$210,000
$230,000
$250,000
Level 1 Level 2 Level 3
Bachelor's Master's PhD, ABD PhD
Job Level Education Base SalaryN 25% Median Mean 75%
Manager,
Level 1
Bachelor's 48 $87,500 $105,000 $108,635 $125,000
Master's 275 $105,000 $120,000 $119,463 $135,000
PhD, ABD 9 $120,000 $135,000 $135,444 $141,000PhD 72 $112,000 $125,000 $127,211 $142,500
Manager,
Level 2
Bachelor's 62 $140,000 $160,000 $159,065 $175,000
Master's 322 $130,000 $150,000 $153,350 $172,000
PhD, ABD 12 $145,500 $165,000 $166,250 $188,000
PhD 137 $145,000 $160,000 $163,515 $180,000
Manager,
Level 3
Bachelor's 18 $200,000 $238,000 $268,667 $275,000
Master's 87 $190,000 $210,000 $229,893 $250,000
PhD, ABD 5 $210,000 $220,000 $221,800 $224,000
PhD 46 $190,000 $215,000 $224,761 $240,000
Figure 17. Base Salary of Managers by Job Level and Education
Figure 18. Median Base Salary of Managers by Job Level and Education
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Special Report:
Compensation for Entry Level Jobs by Education
In a separate analysis, Burtch Works analyzed compensation data for 93 Big Data professionals who
recently completed school and are in their first job earning their starting salary. Not surprisingly,
these et leel salaies deped o hat degee as eaed.
• The mean entry level salary of the entire sample is $63,000.
The mean et leel sala of the suset ho hae just opleted a Bahelo’s degee is
$52,000.
The mean et leel sala of those ho hae opleted a Maste’s degee is $,.
The mean entry level salary of professionals who have earned a Ph.D. is a much greater
$73,000.
61% of all Big Data professionals in their first jobs are eligible for a bonus.
Only 17% of Big Data professionals in their first jobs receive a sign-on bonus. For those who
did, the average bonus is $5,000.
Entry level data scientists were excluded from this sample.
Figure 19. Mean Entry Level Salary by Education
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Bachelor's Master's PhD
Figure 20. Bonuses for Entry Level Jobs
No
39%
Yes
61%
Bonus Eligible
No
83%
Yes
17%
Sign-On Bonus
Bonus Percentage:
3% to 20%
Average Sign-On Bonus:
$5,000
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Compensation by Residency Status
Figures 21 and 22 shows how the median base salaries of Big Data professionals who are not U.S.
citizens vary from the salaries of those who are, controlling for job level.
Unexpectedly, individual contributors at job levels 1 and 2 who are not U.S. citizens have higher
salaries than those who are. There are several possible explanations for this.
• Non-U.S. citizens perform well in school because they would not be at schools in the U.S.
unless they were among the best and the brightest of the youths in their home countries.
Moreover, once here, they focus almost solely on the studies that brought them to the
United States.
• Once they have completed their studies, non-U.S. citizens conduct more thorough job
searches, because they require a job with a company that will sponsor their application for
an H-1B visa and support them as they seek permanent residency.
•
Non-U.S. citizens are more willing to work anywhere in the U.S., which affords them a largerchoice of jobs.
Among more senior managers, salaries of non-U.S. citizens are more similar to those of U.S. citizens.
Among the most senior individual contributors, salaries of non-U.S. citizens are considerably less.
The explanations include:
• By the time individuals born abroad become senior Big Data professionals, they have also
become U.S. citizens. Consequently, the salary data for senior Big Data professionals who
are U.S. citizens includes salaries for many individuals who were once non-U.S. citizens.
•
If a Big Data professional is at senior job level but has not yet obtained U.S. citizenship, it isoften a sign that he has made strategic errors in the advancement of his career, and his
salary might reflect this.
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Job LevelResidency
Status N
Median
Base Salary
Difference from
Citizen Base Salary
Manager,Level 1
Citizen 250 $120,000 0%
Perm Res 90 $122,500 +2%
H-1B 58 $110,000 -8%
F-1/OPT 2 - -
Other 7 $85,000 -29%
Manager,
Level 2
Citizen 397 $154,000 0%
Perm Res 118 $150,000 -3%
H-1B 19 $165,000 +7%
F-1/OPT 1 - -
Other 3 - -
Manager,
Level 3
Citizen 135 $215,000 0%
Perm Res 22 $222,500 +3%
H-1B 1 - -
Job LevelResidency
Status N
Median
Base Salary
Difference from
Citizen Base Salary
Individual
Contributor,
Level 1
Citizen 151 $62,000 0%
Perm Res 33 $71,200 +15%
H-1B 142 $70,000 +13%
F-1/OPT 24 $65,000 +5%
Other 3 - -
Individual
Contributor,
Level 2
Citizen 206 $81,500 0%
Perm Res 124 $87,250 +7%
H-1B 159 $82,500 +1%
F-1/OPT 2 - -
Other 10 $80,000 -2%
Individual
Contributor,
Level 3
Citizen 473 $115,000 0%
Perm Res 144 $110,050 -4%
H-1B 33 $103,000 -10%
F-1/OPT 2 - -
Other 8 $96,000 -17%
Figure 21. Median Base Salary of Individual Contributors by Job Level and
Residency Status
Figure 22. Median Base Salary of Managers by Job Level and Residency Status
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Compensation by Region
Figures 23 through 26 show the distributions of the base salaries of Big Data professionals by
region, controlling for job level.
Regardless of job level, individual contributors employed by firms on the West Coast are
paid the highest salaries. Among those at job level 1, the difference is $15,000. Thedifference declines at more senior job levels.
On the other hand, managers are paid the highest salaries by firms in the Northeast.
The regional differences in salaries are not as great as the regional differences in many costs
of living, such as housing and taxes.
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$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
$100,000
$110,000
$120,000
$130,000
Level 1 Level 2 Level 3
West Coast Mountain Midwest Southeast Northeast
Job Level RegionBase Salary
N 25% Median Mean 75%
IndividualContributor,
Level 1
Northeast 118 $60,000 $65,000 $69,235 $78,000
Southeast 45 $60,000 $65,000 $67,753 $70,000
Midwest 128 $55,000 $65,000 $65,196 $75,000
Mountain 20 $57,500 $65,500 $69,393 $79,000
West Coast 38 $63,000 $80,000 $79,225 $90,000
Individual
Contributor,
Level 2
Northeast 147 $74,000 $86,000 $87,697 $100,000
Southeast 58 $70,000 $78,000 $78,966 $86,000
Midwest 182 $70,000 $85,000 $82,543 $92,000
Mountain 32 $70,500 $83,500 $84,516 $96,000
West Coast 79 $72,000 $90,000 $90,690 $105,000
IndividualContributor,
Level 3
Northeast 204 $100,000 $120,000 $122,352 $135,000
Southeast 81 $90,000 $110,000 $113,049 $130,000Midwest 217 $88,000 $105,000 $110,084 $126,000
Mountain 53 $90,000 $110,000 $113,232 $133,000
West Coast 100 $102,500 $120,000 $126,795 $140,000
Figure 23. Distribution of Base Salaries of Individual Contributors by Job Level and Region
Figure 24. Median Base Salary of Individual Contributors by Job Level and Region
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$50,000
$70,000
$90,000
$110,000
$130,000
$150,000
$170,000
$190,000
$210,000
$230,000
$250,000
Level 1 Level 2 Level 3
West Coast Mountain Midwest Southeast Northeast
Job Level RegionBase Salary
N 25% Median Mean 75%
Manager,Level 1
Northeast 122 $110,000 $130,000 $126,763 $140,000
Southeast 45 $95,000 $115,000 $119,178 $135,000
Midwest 151 $100,000 $117,000 $116,957 $135,000
Mountain 33 $105,000 $115,000 $111,833 $125,000
West Coast 50 $100,000 $115,500 $118,140 $133,000
Manager,
Level 2
Northeast 184 $145,000 $160,000 $165,687 $185,000
Southeast 67 $121,000 $145,000 $143,725 $165,000
Midwest 169 $134,000 $150,000 $151,790 $169,000
Mountain 41 $130,000 $148,000 $147,549 $165,000
West Coast 70 $140,000 $154,000 $163,100 $185,000
Manager,
Level 3
Northeast 63 $203,000 $230,000 $242,254 $265,000
Southeast 16 $192,500 $222,500 $219,063 $247,500
Midwest 46 $189,000 $206,000 $220,146 $241,000
Mountain 5 $210,000 $220,000 $341,000 $250,000
West Coast 28 $185,500 $200,000 $222,000 $228,000
Figure 25. Distribution of Base Salaries of Managers by Job Level and Region
Figure 26. Median Base Salary of Managers by Job Level and Region
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Compensation by Industry
Figures 27 through 30 show distributions of base salaries of Big Data professionals by industry,
controlling for job level
Firms in the tech/telecom industry pay individual contributors high salaries, particularly the
most junior individual contributors. This is partly because so many of these firms are locatedon the West Coast, where the competition for young talent is particularly fierce.
Consulting firms pay high salaries to both individual contributors and managers. This is to
attract and retain professionals to jobs that can require long work days and frequent travel.
Healthcare and pharmaceutical companies are also among those paying the highest base
salaries to Big Data professionals. This too is at least partly because such a large proportion
of these firms are located in the Northeast and on the West Coast, where the cost of living is
high.
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Job Level Industry Base SalaryN 25% Median Mean 75%
IndividualContributor,
Level 1
Advertising/Mktg Services 81 $56,000 $62,000 $63,702 $68,000
Consulting 20 $64,000 $70,000 $75,170 $87,500
CPG 2 - - - -
Financial Services 97 $60,000 $70,000 $72,781 $82,500Healthcare/Pharma 7 $60,000 $67,500 $69,071 $80,000
Outsourcing 6 $60,000 $66,500 $65,833 $72,000
Retail 20 $56,500 $65,000 $65,400 $75,000
Tech/Telecom 12 $74,000 $91,000 $91,125 $105,000
Other 30 $55,000 $61,433 $65,807 $75,000
Individual
Contributor,Level 2
Advertising/Mktg Services 107 $70,000 $80,000 $82,918 $95,000
Consulting 30 $85,000 $95,000 $103,600 $120,000
CPG 1 - - - -
Financial Services 150 $76,000 $85,000 $86,149 $96,000
Healthcare/Pharma 10 $80,000 $93,000 $92,550 $105,000Outsourcing 14 $70,000 $79,000 $79,429 $89,500
Retail 27 $62,500 $76,000 $76,852 $85,000
Tech/Telecom 23 $85,000 $90,000 $93,500 $100,000
Other 36 $75,000 $86,250 $85,528 $95,000
Individual
Contributor,
Level 3
Advertising/Mktg Services 98 $92,000 $107,500 $111,983 $133,000
Consulting 40 $120,000 $130,000 $136,313 $154,000
CPG 12 $105,000 $116,500 $120,625 $134,750
Financial Services 198 $95,000 $110,000 $121,018 $135,000
Healthcare/Pharma 30 $97,000 $113,000 $119,600 $135,000
Outsourcing 4 - - - -Retail 33 $90,000 $100,000 $104,842 $115,000
Tech/Telecom 43 $99,000 $125,000 $123,209 $140,000
Other 43 $82,000 $105,000 $102,826 $120,000
Figure 27. Distribution of Base Salaries of Individual Contributors by Job Level and Industry
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$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
$100,000
$110,000
$120,000
$130,000
Level 1 Level 2 Level 3
Advertising/Marketing Svcs. Consulting Consumer Packaged Goods
Financials Svcs. Healthcare/Pharma Outsourcing
Retail Tech/Telecom Other
Figure 28. Median Base Salary of Individual Contributors by Job Level and Industry
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Job Level Industry Base SalaryN 25% Median Mean 75%
Manager,
Level 1
Advertising/Mktg Services 96 $106,500 $120,000 $120,254 $135,000
Consulting 27 $115,000 $130,000 $129,330 $150,000
CPG 6 $120,000 $127,500 $125,667 $134,000
Financial Services 122 $105,000 $120,000 $121,804 $135,000Healthcare/Pharma 16 $118,000 $135,000 $133,040 $144,500
Outsourcing 5 $85,000 $96,000 $101,900 $100,000
Retail 26 $105,000 $111,000 $115,519 $130,000
Tech/Telecom 22 $115,000 $122,500 $123,477 $130,000
Other 28 $96,500 $116,500 $115,732 $128,000
Manager,
Level 2
Advertising/Mktg Services 137 $140,000 $153,000 $158,779 $178,000
Consulting 35 $150,000 $175,000 $168,957 $190,000
CPG 20 $147,500 $161,250 $166,837 $177,000
Financial Services 129 $126,000 $147,000 $150,881 $165,000
Healthcare/Pharma 25 $140,000 $155,000 $154,340 $165,000Outsourcing 6 $135,000 $150,000 $150,833 $170,000
Retail 40 $130,000 $151,000 $154,450 $173,500
Tech/Telecom 24 $137,000 $150,000 $162,333 $195,000
Other 34 $139,000 $149,500 $152,971 $170,000
Manager,
Level 3
Advertising/Mktg Services 54 $199,000 $225,000 $227,504 $250,000
Consulting 12 $215,000 $247,500 $289,750 $298,500
CPG 5 $220,000 $225,000 $233,000 $260,000
Financial Services 15 $201,000 $225,000 $278,400 $250,000
Healthcare/Pharma 14 $195,000 $200,000 $215,000 $250,000
Outsourcing 2 - - - -Retail 9 $180,000 $190,000 $204,222 $237,000
Tech/Telecom 7 $186,000 $195,000 $204,714 $210,000
Other 7 $125,000 $195,000 $177,857 $220,000
Figure 29. Distribution of Base Salaries of Managers by Job Level and Industry
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$90,000
$110,000
$130,000
$150,000
$170,000
$190,000
$210,000
$230,000
$250,000
Level 1 Level 2 Level 3
Advertising/Marketing Svcs. Consulting Consumer Packaged Goods
Financials Svcs. Healthcare/Pharma Outsourcing
Retail Tech/Telecom Other
Figure 30. Median Base Salary of Managers by Job Level and Industry
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Compensation by Gender
The Burtch Works Study has shown that base salaries of Big Data professionals vary with job level,
education, residency status, region and industry. The salaries of Big Data professionals also vary
with gender, but not nearly as much as the salaries of practitioners of other professions.
Across the entire U.S. labor market, the average compensation of a woman is 77% of the averagecompensation of a man. However, among Big Data professionals at the same job level, the ratio of
the median salary of women to the median salary of men is no smaller than 90%. For the more
junior job levels, at which most Big Data professionals are employed, the ratio is never less than
94%.
Figure 31. Median Base Salary by Job Level and Gender
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
$180,000
$200,000
$220,000
IC, Level 1 IC, Level 2 IC, Level 3 MG, Level 1 MG, Level 2 MG, Level 3
Men Women
98%
94%
91%
96%
97%
90%
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Section 5
SPECIAL REPORT: SALARY INCREASES OF
BIG DATA PROFESSIONALS
WHO CHANGE JOBS
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Base Salary Change by Job Title
Figure 33 shows the average increase in base salary by new title of the Big Data professionals who
changed jobs.
• Salary increases were far larger for professionals taking senior jobs. The average increase
for a professional taking a job as a Vice President was almost $25,000, about twice theincrease of a professional taking a job as a Manager.
• However, percentage salary increases were larger for professionals moving to junior jobs
than for those moving to senior jobs. The average percentage increase for a professional
taking a job with an Analyst title was 14.1%, while the average percentage increase for a
professional taking a Vice President job was 12.6%.
Figure 33. Average Salary Increase by New Job Title
$0
$5,000
$10,000
$15,000
$20,000
$25,000
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Base Salary Change by Education
Figure 34 shows the average increase in base salary by education for the Big Data professionals who
changed jobs.
• Salary increases were larger for a professional changing jobs if they had an advanced
degree. The average increase for professionals with a Ph.D. was over $18,000, while it wasappoiatel $, fo those ith a Bahelo’s degee.
• Percentage salary increases were also larger for professionals with a Ph.D. The average
increase for a professional with a Ph.D. changing jobs was 16%, while it was 14% for those
ith a Bahelo’s degee.
Figure 34. Average Percentage Salary Increase by
$0
$5,000
$10,000
$15,000
$20,000
$25,000
Bachelor's Master's PhD
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Section 6
WHERE DO WE GO FROM HERE?
Recruiting & Retaining Big Data Professionals
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Recruiting & Retaining Big Data Professionals
Since the Great Recession began, most professions have experienced weak demand for their
practitioners. The Big Data profession has been an exception. Now, as economic growth in the
U.S. accelerates, it will become even more difficult to hire and retain Big Data professionals. What
advice does Burtch Works have?
• Firms must increase pay for Big Data professionals. Salary bands for Big Data professionals
have not changed much in the past several years. Firms will need to shift these bands to
successfully retain Big Data professionals. As the Burtch Works Study has shown, these
professionals currently realize large increases in compensation when they change jobs.
• Firms must be willing to sponsor applications for visas or transfers of visas of Big Data
professionals who are foreign nationals.
• To be more effective, online sourcing of candidates must be more targeted. Corporate
sourcing specialists are intensively using LinkedIn to identify candidates for Big Data jobs at
their firms. However, because they then contact so many prospects, Big Data professionalshae egu to oplai of euite fatigue ad often decline to discuss job opportunities
when contacted.
• Those hiring Big Data professionals must prioritize the skills and experience required,
because it will become increasingly difficult to attract individuals with all of the desired
qualifications.
• Firms should consider the alternative of training: equipping current staff with the
methodological and software skills needed to exploit Big Data.
And, of course, those seeking Big Data professionals should’t e elutat to contact Burtch Worksfor advice and support:
Burtch Works LLC
1560 Sherman Ave Suite 1005
Evanston, IL 60201
Call: 847-440-8555
Email: [email protected]
mailto:[email protected]:[email protected]:[email protected]
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Section 7
APPENDIX
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Glossary of Terms
This section provides definitions of terms used in this report.
ABD (All-but-dissertation). ABD is a level of education. A person whose level of education level is ABD
has completed all coursework for a Ph.D. except for a dissertation.
Base Salary. A idiidual’s goss aual ages, eludig aiale o oe-time compensation such asrelocation assistance, sign-on bonuses, bonuses, and long-term incentive plan compensation.
Big Data Professionals. Individuals who can apply sophisticated quantitative skills to data describing
transactions, interactions, or other behaviors of people to derive insights and prescribe actions. They
ae distiguished fo the uats of the past the shee uatit of data o hih the opeate, a
abundance made possible by new opportunities for measuring behaviors and advances in technologies
for the storage and retrieval of data.
Bonus. Short-term variable compensation usually awarded annually, such as individual or company
performance-based bonuses. This does not include long-term incentive plan compensation or awards of
stock or stock options.
Data Scientist. A Big Data professional who has both the proficiency for data management required to
make Big Data accessible and also the analytical skills for deriving useful information from Big Data.
Entry-level job. A job available to individuals who have no prior work experience, but usually have just
earned an undergraduate or graduate degree.
F-1/OPT. A residency status that allows a foreign undergraduate or graduate student who has a non-
immigrant F-1 student visa to work in the U.S. without obtaining an H-1B visa. The student is required
to have either completed his degree or pursued it for at least nine months.
Geographic Region. One of five groups of states that together comprise the entire United States. These
five groups of states – Northeast, Southeast, Midwest, Mountain and West Coast – are shown in Figure 3
on page 10.
H-1B. A non-immigrant visa that allows a U.S. firm to temporarily employ a foreign worker in a specialty
occupation for a period of three years, which is extendable to six and beyond. If a foreign worker withan H-1B visa quits or loses his job with the sponsoring firm, the worker must either find a new employer
to sponsor an H-1B visa, be granted a new non-immigrant status, or leave the United States.
Individual Contributor. An employee who does not manage other employees. Individual contributors
among the Big Data professionals in the Burtch Works sample have all been assigned to one of three
levels:
Level 1: Responsible for learning the job; hands-on with analytics and modeling; 0- eas’
experience
Level 2: Hands-on with data, working with more advanced problems and models; may help train
Analysts; 4-8 years of experience
Level 3: Cosideed a aaltis “ujet Matte Epet; etos ad tais aalsts; + eas’experience
Industry. One of nine groups of firms employing most data professionals. These nine industries are
Advertising/Marketing Services, Consulting, Consumer Packaged Goods, Financial Services,
Healthcare/Pharmaceuticals, Outsourcing, Retail, Tech/Telecom and Other.
Advertising/Marketing Services: An industry consisting of firms that provide services to other
firms that include advertising, market research, media planning and buying, and marketing
analysis.
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Consulting: Industry that includes both large copoatios ad sall outiue fis that
provide professional advice to the managers of other firms.
Consumer Packaged Goods: Companies whose products are sold quickly and at relatively low
cost, including non-durable goods (e.g. groceries, toiletries) and lower-quality consumer
electronics.
Financial Services: Firms that provide services related to the finance industry, which
encompasses a broad range of organizations that manage money including banks, insurance
companies, and credit card organizations.
Healthcare/Pharmaceuticals: Sector that includes companies that provide patients with
healthcare services, and firms that manufacture medicinal drugs.
Outsourcing: Companies whose primary workforce is contracted by their clients, in order to
move labor out of the internal business process to a third party organization. Many outsourcing
companies utilize off-shore resources to complete work for clients.
Retail: Organizations that purchase goods from a manufacturer to be sold for profit to the end-
consumer.
Tech/Telecom: Industry that includes companies that provide telecommunications services in
addition to organizations that focus on creating or distributing technology products or services.
Other: Companies whose industry falls outside of the eight categories delineated above, such as
airline companies, distribution firms, media, and entertainment.
Manager. An employee who manages the work of other employees. Managers among the Big Data
professionals in the Burtch Works sample have all been assigned to one of three levels:
Level 1: Tactical manager who leads a small group within a function, responsible for executing
limited-scale projects or tasks within a project; typically responsible for 1-3 direct reports or
matrix individuals.
Level 2: Manager who leads a function and manages a moderately sized team; responsible for
executing strategy; typically responsible for 4-9 direct reports or matrix individuals.
Level 3: Member of senior management who determines strategy and leads large teams;
manages at the executive level; typically responsible for 10+ direct reports or matrix individuals.
Mean. Also known as the average, it is the sum of a set of values divided by the number of values. For
example, the mean of N salaries is the sum of the salaries divided by N.
Median. The value obtained by ordering a set of numbers from smallest to largest and then taking the
value in middle, or, if there are an even number of values, by taking the mean of the two values in the
middle. For example, the median of N salaries is the salary for which there are as many salaries that are
smaller as there are salaries that are larger.
N. The number of observations in a sample, sub-sample or table cell.
OPT. See F-1/OPT.
Permanent Resident. A residency status that allows a foreign national to permanently live and work in
the United States. Those with this status have a United States Permanent Residence Card, which is
known informally as a green card.
Salary Study. A study conducted to measure the distributions by salary of those in specific occupations.
Traditionally, these studies have been executed by obtaining salary data from the human resources
departments of firms employing professionals in those occupations rather than by interviewing those
employees themselves.
STEM. Acronym for the fields of Science, Technology, Engineering and Mathematics.
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ABOUT BURTCH WORKS
Burtch Works is a specialized executive recruiting firm dedicated to placing highly qualified quantitative
professionals in analytics roles nationwide. By maintaining constant contact with hundreds of staffers,
hiring managers and human resources professionals every month, we are able to follow developing
trends in the high growth field of marketing analytics. In addition, we are continuously tracking talent
movement and industry changes that are creating new jobs every day. Leveraging our massive and
unique network of quantitative professionals, we can ensure that we find the best possible fit for both
our candidates and our clients.
We pride ourselves on our reputation as the premier source of the best jobs in the quantitativemarketing field, and we welcome complete and thorough feedback on our work as we go through the
recruiting process. If you are looking to build a first class analytics staff or if you are considering a job
change yourself, we encourage you to contact us.
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