The Burtch Works Study...6 The Burtch Works Study Salaries of Data Scientists & Predictive Analytics...

6

The Burtch Works Study

Salaries of Data Scientists &

Predictive Analytics Professionals June 2019

Linda Burtch

Managing Director

© 2019 Burtch Works LLC THE BURTCH WORKS STUDY 2

Section 1: Introduction 3

Defining Data Science & Predictive Analytics Professionals 4

2019 Data Science & Predictive Analytics Market Trends 5

This Year’s Compensation Insights 7

About Burtch Works 9

Section 2: Compensation Changes 10

The Sample 11

How Changes in Compensation Were Measured 11

Changes in Base Salaries 12

Data Scientists vs. Others in Predictive Analytics 17

Section 3: Demographic Profile 19

Education 20

Residency Status 24

Region 29

Industry 32

Gender 36

Years of Experience 39

Section 4: Appendix A/Study Objective & Design 40

Study Objective 41

Why The Burtch Works Studies Are Unique 41

The Sample 41

How Changes in Compensation Were Measured 42

Identifying Data Science & Predictive Analytics Professionals 42

Completeness & Age of Data 44

Data Science & Precditive Analytics Segmentation 45

Section 5: Appendix B/Glossary 47

Glossary of Terms 48

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Table of

Contents

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© 2019 Burtch Works LLC THE BURTCH WORKS STUDY | DATA SCIENCE & PREDICTIVE ANALYTICS 3

SECTION 1

Introduction


Defining Data Science & Predictive Analytics Professionals

We have historically defined predictive analytics professionals (PAPs) as those who can “apply

sophisticated quantitative skills to data describing transactions, interactions, or other behaviors

to derive insights and prescribe actions”.

PAPs are distinguished from business intelligence professionals or traditional financial analysts by

the enormous quantity of data with which they work, well beyond what can be managed in

Excel. This definition also encompasses data scientists, who are examined separately from PAPs

in this study because of their distinguishing ability to work with unstructured data, which results in

different compensation.

Data scientists, as we define them, are a subset of PAPs who have the computer science skills

necessary to acquire and clean or transform unstructured or continuously streaming data,

regardless of its format, size, or source. Unstructured data may include: video streams, audio

data, social media web scrapes, sensor data, raw log files, or long blocks of written language.

This report focuses on both PAPs and the Data Scientist subset, showing compensation and

demographic information for both groups separately and in comparison. For more information on

how we identified predictive analytics professionals and data scientists, see Appendix A on page

42.


2019 Data Science & Predictive Analytics Market Trends

With the distinction between roles in predictive analytics and data science growing fuzzier by the

day, this year, for the first time, we’ve combined our separate Data Science and Predictive

Analytics salary reports into one.

We’ve always regarded data scientists as a specialized subset of predictive analytics

professionals, and our hope in presenting these two sets of data side-by-side (instead of in

separate studies) is to show some of the interesting comparisons between the two groups that

we’ve noticed over the past few years.

Burtch Works has typically segmented data scientists and predictive analytics professionals

because of skillset differences that led to differing salary bands. As we’ve defined them, data

scientists work primarily with unstructured or streaming data and therefore command higher

salaries than others in predictive analytics that mostly focus on structured data. Although the two

areas are becoming more blended as of late, there are a number of reasons why we’ve

continued to analyze them separately that we’ll be highlighting throughout this report. For

complete definitions on how we segmented these professionals, see page 42.

For our hiring market trend analysis in this section, we wanted to highlight the trends that are

affecting the quantitative hiring market from our unique vantage point as recruiters working with

such a wide variety of quantitative teams.

Quantitative Hiring Market Trends

Predictive analytics tools are becoming more robust

Open source tools and vendor-provided solutions have made implementing what used to be a

complex and rigorous math and computer science problem more accessible to a wider range of

individuals. While data science tools are far from a “point and click” stage, they’re maturing

quickly.

Educational backgrounds are continuing to shift

Alongside the maturation of data science tools and an increasing number of education options

(such as bootcamps, online courses, and Master’s programs), we’ve noticed some shifting in

educational backgrounds. Among predictive analytics professionals with 0-3 and 4-8 years of work

experience, there was a noticeable uptick in the proportion of these segments whose highest

degree was a Bachelor’s.

Quantitative professionals have more options than ever

The accelerating proliferation of data science and analytics initiatives across the country and

across a multitude of industries has led to quantitative professionals having more options than

ever. Not only geographically and industry-wise, but options have also become more technically

diverse, as more industries continue to mature and explore new potential use cases for their data

teams.


Among quantitative professionals there is increased interest in so-called “mission driven

roles” that have some sort of societal or environmental impact (such as using data to

improve health outcomes, energy efficiency, or to support non-profit work).

Emergence of the “data science citizen” role

With many quantitative teams increasing their size and scope, many businesses are grappling with

the challenge of how best to liaise between the technical team and other business unit

stakeholders. Companies sometimes staff up with a so-called “data science citizen”, who is

typically a professional with a data science or analytics foundation who has sufficient technical

depth along with the business acumen necessary to translate between the quantitative team and

professionals in other areas like the Marketing or Finance team, or even those in the C-Suite.

These professionals, who may have a range of job titles, are the stewards of data science

initiatives. They use their expertise to be representatives for the quantitative team and free up

data scientists to do what they do best: advanced technical analysis. Both types of roles are

important to a successful quantitative team, and we’re seeing more interest in the data science

citizen role as team sizes continue to expand.

Companies turn to satellite offices to strengthen their bid for talent

For those looking to hire data science and analytics talent, it’s no secret that the location of your

data science team can help or hinder your hiring results. We’ve seen many companies opening

satellite offices in multiple urban areas to increase their role’s marketability.

Major companies in San Francisco especially are continuing to expand their talent

outreach with satellite offices as companies struggle to attract & retain talent in high cost

of living areas.

Technical screens/testing are becoming a staple in the quantitative hiring process

With more variety in educational backgrounds than ever before, technical screens and testing

are swiftly becoming “must haves” in the hiring toolkit. Most quantitative professionals are still

coming from more traditional educational backgrounds such as a Master’s or PhD program.

However, many have also supplemented their skillset with online courses or coding bootcamps in

order to stay up-to-date.

Seeking to better evaluate talent that come with a wide variety of educational backgrounds,

many hiring managers are relying on testing to measure technique and tool fluency. Additionally,

we continue to see case studies used to assess problem-solving capabilities and business acumen.

With all of these methods, it’s important to remember to keep the hiring process moving and keep

candidate time commitments reasonable in order to compete in today’s hot market.

As demand increases for specialized skillsets, the search becomes more challenging

Companies looking to drill down on specific use cases are focusing more on data scientists that

specialize in particular areas, such as NLP (natural language processing), Computer Vision (image

processing), or even specific domain experience like AdTech or IoT (internet of things). Of course,

as demand becomes more specialized, this also narrows the potential talent pool to draw from.


The market used to be more focused on do-it-all “unicorns” which are also difficult to find

(on the opposite end of the spectrum), but now that analytics at many firms is maturing,

organizations are pivoting to seek out more specific skillsets that can elevate their

capabilities to the next level.

With more companies expanding data science capabilities, a push for visionary leaders

As so many organizations recognize the value add of data-driven decision making, there is a

strong need for leaders who not only have compelling leadership skills and a strong understanding

of analytics, but who are also well-versed in new or emerging techniques and who can explore

new opportunities for the business. Having a strong analytics leader at the helm who can

recognize potential use cases and push what’s possible is crucial to success in today’s competitive

business landscape.

Salary disclosure bans continue to spread and change hiring processes

Continuing from last year, laws preventing employers from asking potential candidates about their

salary history are spreading to more and more places. This provides challenges for our data

collection for this study, but it also can complicate the hiring process. Without salary disclosure,

there is often additional back and forth that can lengthen an otherwise streamlined hiring process,

and it can be more difficult for employers and candidates to be on the same page about

compensation.

This Year’s Compensation Insights

Compensation and demographic data of 1,840 PAPs and 421 data scientists is shared in this

report, an update to our May 2018 release of The Burtch Works Study: Salaries of Data Scientists

and our October 2018 release of The Burtch Works Study: Salaries of Predictive Analytics

Professionals. The data shared here was collected during the months following the collection

periods of last years’ studies and ending April 2019.

Our salary studies report base salary variations of PAPs and data scientists, both individual

contributors and managers. We also report how base salaries have changed since last year’s

study. Finally, the report explains how salaries of PAPs and data scientists vary based on several

characteristics including job level, industry, region, education, residency status, and gender.

For detailed analysis on this year’s compensation trends, see Section 2 starting on page 10. For

demographic trends and insights, see Section 3 starting on page 19.

Predictive Analytics Professionals

The median base salary of individual contributors at level 1 is $80,000 and increases, based on job

level, up to $132,000 for those at level 3. PAPs in management roles earn higher base salaries than

individual contributors (see Figure 3 on page 15). Managers at level 1 earn a median base salary

of $130,000, which increases to $248,500 for managers at level 3 (see Figure 5 on page 16).

When compared to 2018 data, median base salaries for individual contributors showed an

increase at all levels. For level 1 and level 2 managers, median base salary remained steady while

level 3 managers saw an increase. Mean salaries saw an increase at all levels across both

individual contributors and managers (see Figure 3 on page 15 and Figure 5 on page 16).

Data Scientists


For data scientists, median base salaries for individual contributors range from $95,000 at level 1 to

$167,000 at level 3 (see Figure 4 on page 15). For managers, median base salaries ranged from

$146,000 at level 1 to $250,000 at level 3 (see Figure 6 on page 16).

In comparison to 2018 data, median base salaries for 2019 have either remained steady or risen

slightly at all levels (see Figure 2 on page 14). Salary means increased at all levels, and salaries at

the top quartile increased significantly in some cases (see Figure 4 on page 15 and Figure 6 on

page 16). This may indicate that salary ranges are stretching and could be a sign of future salary

growth.

While the median base salaries show that data scientists outearn other predictive analytics

professionals at all levels, the differences are the most evident for individual contributors where

data scientists earn from 19-34% more than PAPs (see Figure 7 on page 18).

For additional details about how salaries vary from last year, see Section 2. For complete

information about how salaries vary by demographic characteristics, see Section 3.


About Burtch Works

Burtch Works Executive Recruiting is the leading resource for quantitative talent, job opportunities,

and information about hiring and compensation trends in this industry. Our team has decades of

experience in their quantitative specialties, which include predictive analytics, data science, data

engineering, quantitative business analytics, web analytics, credit/risk analytics, marketing

research, and many more. Each recruiter is well-versed in the subtle nuances of their area of

expertise, allowing them to closely follow the talent movement and hiring trends unique to each

area, and find individuals perfectly suited to each role.

As data-driven practices have become a necessary strategy to remain competitive, the

quantitative fields continue to experience incredible growth. Burtch Works has built a diverse

network of tens of thousands of professionals to address the growing number of quantitative

positions nationwide, and this network is the foundation of a business built on long-standing

relationships with both candidates and clients. Linda Burtch, Burtch Works’ Founder and Managing

Director, emphasizes that the most rewarding aspect of her career is creating the perfect match,

and she has established a dedicated team of recruiters who share this vision for Burtch Works.

Over her 35+ years of recruiting in quantitative disciplines, Linda Burtch has developed an

especially comprehensive understanding of the analytics fields. She often writes on topics of

interest to the quantitative community, and has maintained a blog on hiring trends for over 10

years, keeping her finger on the pulse of current trends. She has been interviewed for her insights

on the data science and analytics talent market by The New York Times, The Wall Street Journal,

CNBC, Mashable, Forbes, The Chicago Tribune, The Economist, Bloomberg, Analytics Magazine,

InformationWeek, Hunt Scanlon, and many more. This year Burtch Works is proud to once again

have been recognized by Forbes as one of America’s Best Recruiting Firms.

By maintaining such strong relationships with candidates and clients, Burtch Works has the unique

opportunity to examine hiring and compensation trends over time, and publishes several highly-

anticipated studies each year that investigate demographic and compensation data for

predictive analytics, marketing research, and data science professionals. The Burtch Works Studies

provide an exceptional vantage point on compensation for these professionals across the

country, and contain critical information both for individuals mapping their career strategy, and

for hiring managers hoping to recruit and retain outstanding personnel to their teams.


SECTION 2

Data Science & Predictive

Analytics Professionals:

Compensation Changes


The Sample

This sample contains 1,840 predictive analytics professionals (PAPs) and 421 data scientists of the

nearly 40,000 quantitative professionals with whom Burtch Works maintains contact. Burtch Works

collected the data for this study during interviews conducted over the months immediately

following the period of interviews for the 2018 studies, with data collection ending in April 2019.

Professionals were included in the sample only if (1) they satisfied Burtch Works’ criteria for PAPs

and data scientists, and (2) Burtch Works obtained complete information about that individual’s

compensation, demographic, and job characteristics.

For more details on how Burtch Works distinguishes between PAPs and the data scientist subset,

see Identifying Data Science & Predictive Analytics Professionals on page 42.

How Changes in Compensation Were Measured

While some of the 1,840 PAPs and 421 data scientists in this sample were also in the samples for

our previous studies (published in 2013, 2014, 2015, 2016, 2017, and 2018), others were not.

Therefore, changes in compensation were not measured by differencing current compensation

and compensation reported for the previous study and then taking medians (and other

percentiles) of the differences. Instead, changes were measured by comparing medians (and

other percentiles) of current compensation to those reported in last year’s study.


Changes in Base Salaries


For individual contributors at all levels, median base salaries increased. The largest

increase was at level 1 (4%) while levels 2 and 3 showed 2% increases (see Figure 1 on

page 13).

Median salaries for managers at levels 1 and 2 held steady while level 3 managers saw

a 4% increase in median base salary (see Figure 1 on page 13).

Base salary means increased across levels for PAPs (see Figure 3 on page 15 and Figure

5 on page 16).

Data Scientists

Median base salaries for data scientists held steady or increased slightly (by 1-3%)

across all levels (see Figure 2 on page 14). The greatest increase in median base salary

was for managers at level 2 (3%).

At all levels, the top quartile showed increases with the largest being 11% for level 3

managers (see Figure 4 on page 15 and Figure 6 on page 16). So while medians were

fairly steady, the top salary ranges show a shift upward as a whole.

Base salary means increased at least slightly at all levels with the smallest increase for

level 1 managers (<1%) and the largest for level 1 individual contributors (5%) (see

Figure 4 on page 15 and Figure 6 on page 16).


$0 $50,000 $100,000 $150,000 $200,000 $250,000

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3Manager

Level 3

Individual

Contributor

Level 3

Individual

Contributor

Level 2

Individual

Contributor

Level 1

Manager

Level 2

Manager

Level 1

Figure 1 Comparison of Predictive Analytics Professionals’ Median Base Salaries by Job Category

2019

2018

+4%

0%

0%

+2%

+2%

+4%

*See page 45 for job category definitions.


Figure 2 Comparison of Data Scientists’ Median Base Salaries by Job Category

$0 $50,000 $100,000 $150,000 $200,000 $250,000

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3Manager

Level 3

Manager

Level 2

Manager

Level 1

Individual

Contributor

Level 3

Individual

Contributor

Level 2

Individual

Contributor

Level 1

0%

+3%

+1%

+1%

+1%

0%


2019

2018


Job Level Year 25% Median Mean 75% N

Individual Contributor

Level 1

2019 $70,000 $80,000 $81,607 $90,000 308

2018 $68,125 $76,750 $79,358 $86,000 262

Change +3% +4% +3% +5%


Level 2

2019 $88,400 $97,000 $100,532 $114,000 345

2018 $85,000 $95,000 $98,425 $110,000 359

Change +4% +2% +2% +4%


Level 3

2019 $120,000 $132,000 $140,032 $153,000 297

2018 $114,250 $130,000 $132,908 $150,000 310

Change +5% +2% +5% +2%



Level 1

2019 $85,000 $95,000 $97,749 $110,000 73

2018 $80,000 $95,000 $94,987 $105,000 97

Change +6% 0% +5% +5%


Level 2

2019 $115,000 $130,000 $133,324 $150,000 104

2018 $114,055 $128,750 $130,700 $144,500 107

Change +1% +1% +2% +4%


Level 3

2019 $147,000 $167,000 $171,755 $200,000 55

2018 $150,000 $165,000 $168,170 $194,000 47

Change -2% +1% +2% +3%

Figure 3 Change in Base Salaries of Predictive Analytics Individual Contributors by Job Level

Figure 4 Change in Base Salaries of Data Science Individual Contributors by Job Level




Manager

Level 1

2019 $120,000 $130,000 $133,742 $148,800 282

2018 $115,000 $130,000 $127,560 $140,000 347

Change +4% 0% +5% +6%

Manager

Level 2

2019 $160,000 $180,000 $180,605 $200,000 432

2018 $160,000 $180,000 $179,223 $195,000 368

Change 0% 0% +1% +3%

Manager

Level 3

2018 $220,000 $248,500 $257,591 $285,000 176

2017 $220,000 $240,000 $247,190 $273,000 145

Change 0% +4% +4% +4%


Manager

Level 1

2019 $130,000 $146,000 $146,475 $157,750 68

2018 $132,000 $145,000 $146,133 $155,000 49

Change -2% +1% +<1% +2%

Manager

Level 2

2019 $175,000 $190,000 $191,831 $215,000 77

2018 $170,000 $185,000 $190,565 $200,000 69

Change +3% +3% +1% +8%

Manager

Level 3

2019 $230,000 $250,000 $257,443 $276,400 44

2018 $238,500 $250,000 $247,633 $250,000 30

Change -4% 0% +4% +11%

Figure 5 Change in Base Salaries of Predictive Analytics Managers by Job Level


Figure 6 Change in Base Salaries of Data Science Managers by Job Level


Compensation | Data Science vs. Others in Predictive Analytics

Burtch Works differentiates data scientists from other predictive analytics professionals (see

Identifying Data Scientists & Predictive Analytics Professionals on page 42), and reports their

respective salaries in this study. Historically, data science salaries are quite different than those

seen in predictive analytics, and even though there are some indications of the two groups

blending, the compensation trend continues this year.

In every job category, higher median base salaries continue to be seen among data

scientists when compared to other predictive analytics professionals.

The difference in base salaries is largest among individual contributors, where data

scientists earn from 19% to 34% more than other predictive analytics professionals (see

Figure 7 on page 18). Level 1 individual contributors, for instance, earn a median base

salary of $95,000 in data science and $80,000 in other predictive analytics roles (see

Figures 3 and 4 on page 15).

For managers, the difference in median base salaries is less pronounced. However,

data science managers still earn more than others within predictive analytics.

Depending on the job level, managers within data science have median base salaries

that are 1% to 12% higher than others within predictive analytics (see Figure 7 on page

18).

There are several factors which impact this pay difference:

Data scientists possess more specialized data skills that allow them to work with

large, unstructured or streaming datasets (see Identifying Data Scientists &

Predictive Analytics Professionals on page 42).

Triple the percentage of data scientists hold a PhD compared to those in predictive

analytics: 47% vs. 15% (see Figure 8 on page 21). Since professionals with a PhD

tend to earn more, this is a contributing factor in higher compensation for data

scientists.

There continues to be considerable attention on the data science profession and

high demand for these professionals, leading to increased competition for talent

and high salaries in comparison to PAPs.

How We Define Data Scientists vs.

Others in Predictive Analytics

Burtch Works considers a data scientist to be a specific type of predictive analytics professional. Both

groups analyze data to glean insights and prescribe action, but data scientists focus on cleaning and

analyzing unstructured or streaming data, using sophisticated computer science and programming skills

that are not typically seen in the profiles of other predictive analytics professionals. In short, the two

groups’ skillsets and experience focus on the following:

See page 42 for more information on how we identify data scientists and predictive analytics professionals.

Data Scientists:

Quantitative skills

Structured and unstructured, streaming data

Computer science/coding

Other Predictive Analytics Professionals:

Quantitative skills

Structured data


$0 $50,000 $100,000 $150,000 $200,000 $250,000

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3Manager

Level 3

Individual

Contributor

Level 3

Individual

Contributor

Level 2

Individual

Contributor

Level 1

Manager

Level 2

Manager

Level 1

Figure 7 Median Base Salaries of Data Scientists vs. Others in Predictive Analytics

Data Scientists

Other Predictive Analytics Professionals

+1%

+6%

+12%

+27%

+34%

+19%



SECTION 3

Data Science &

Predictive Analytics

Professionals:

Demographic Profile


Demographics & Compensation | Education


86% of PAPs sampled hold an advanced degree. 71% hold a Master’s degree and

another 15% hold a PhD (see Figure 8 on page 21).

At most job levels, PAPs earn a higher base salary when they hold an advanced

degree, and PAPs with a Ph.D. outearn those with a Master’s at every level (See

Figure 10 on page 22 and Figure 12 on page 23).

For both PAPs and Data Scientists, the most common area of study (based on highest

degree earned) was mathematics/statistics. However, PAPs were far more likely than

data scientists to hold a business or economics degree (see Figure 9 on page 21)

Data Scientists

Data scientists are even more likely to hold an advanced degree (see Figure 8 on

page 21), with 94% of those sampled having a Master’s or PhD (47% hold a Master’s

and another 47% hold a PhD as their highest degree).

PhD data scientists earn higher median base salaries those with a Master’s as their

highest degree at every level except level 2 and level 3 managers, where in some

cases work experience and management skills may play a larger role than education

(see Figure 11 on page 22 and Figure 13 on page 23).

Data scientists differ from PAPs in the areas of study (based on highest degree) that

they come from. While for both groups mathematics/statistics was the largest

percentage, data scientists were far more likely than PAPs to come from a computer

science, engineering, or natural science educational background (see Figure 9 on

page 21).


0%

10%

20%

30%

40%

50%

60%

70%

Bachelor's Master's PhD

Pe

rce

nta

ge

of

Pro

fess

ion

als

Figure 8 Distribution of Data Scientists & Predictive Analytics Professionals by Education


Professionals

Data Scientists

0% 10% 20% 30%

Natural Science

Computer Science

Social Science

Engineering

Economics

Business

Mathematics/Statistics

Figure 9 Distribution of Data Science & Predictive Analytics Professionals by Area of Study

25%

12%

18%

8%

21%

16%

4%


Data Science

34%

4%

9%

28%

7%

5%

4%

14%

4%


Job Level Education Base Salary

25% Median Mean 75%

Individual

Contributor

Level 1

Bachelor’s $70,000 $80,000 $78,615 $87,000

Master’s $70,000 $80,000 $80,737 $90,000

PhD $83,500 $95,000 $95,778 $103,500

Individual

Contributor

Level 2

Bachelor’s $85,000 $95,000 $97,269 $110,000

Master's $87,500 $95,000 $99,435 $110,000

PhD $96,000 $115,000 $113,000 $130,000

Individual

Contributor

Level 3

Bachelor’s $110,000 $120,000 $124,414 $135,000

Master’s $120,000 $130,000 $138,043 $152,000

PhD $130,000 $140,000 $159,956 $170,000


25% Median Mean 75%

Individual

Contributor

Level 1

Master's $81,000 $90,000 $92,222 $100,000

PhD $90,000 $101,000 $106,365 $118,750

Individual

Contributor

Level 2

Master's $115,000 $125,000 $128,713 $140,000

PhD $118,375 $137,000 $137,761 $155,000

Individual

Contributor

Level 3

Master's $145,000 $160,000 $168,500 $200,500

PhD $150,000 $180,000 $177,020 $200,000

Figure 10 Distribution of Base Salaries of Predictive Analytics Individual Contributors by Job Level

& Education

Figure 11 Distribution of Base Salaries of Data Science Individual Contributors by Job Level &

Education



25% Median Mean 75%

Manager

Level 1

Bachelor’s $119,000 $124,000 $127,759 $137,000

Master's $120,000 $130,000 $133,483 $148,000

PhD $126,000 $140,000 $138,908 $155,000

Manager

Level 2

Bachelor’s $151,250 $172,500 $172,002 $190,000

Master’s $160,000 $175,500 $179,940 $199,750

PhD $170,000 $186,750 $188,083 $201,250

Manager

Level 3

Bachelor’s $209,000 $245,000 $270,917 $292,500

Master's $215,500 $240,000 $249,318 $275,000

PhD $240,000 $250,000 $271,643 $300,000


25% Median Mean 75%

Manager

Level 1

Master's $133,000 $140,200 $143,230 $150,500

PhD $130,000 $146,000 $147,328 $161,250

Manager

Level 2

Master's $172,500 $190,000 $192,065 $213,000

PhD $175,000 $190,000 $189,805 $214,000

Manager

Level 3

Master's $215,000 $250,000 $243,639 $263,750

PhD $232,000 $250,000 $268,045 $278,750

Figure 12 Distribution of Base Salaries of Predictive Analytics Managers by Job Level & Education

Figure 13 Distribution of Base Salaries of Data Science Managers by Job Level & Education


Demographics | Residency Status


27% of PAPs sampled are non-U.S. citizens with permanent residency or an F-1/OPT, H-

1B, or another visa which allows them to work in the U.S. (see Figure 14 on page 25).

Among level 1 individual contributors, 33% of PAPs sampled have a visa or

permanent residency. At the senior management level (manager level 3), this

decreases to 13% (see Figure 15 on page 26).

When examining the individual contributor level 2 sample more closely, we found

those professionals with a visa are more likely to have an advanced degree (at the

IC-2 level, 96% of professionals with an H-1B visa held an advanced degree, while

78% of citizens held an advanced degree). This likely accounts for higher salaries

earned by H-1Bs at the IC-2 level (see Figure 17 on page 27).

Previously, we have seen level 1 individual contributors with H-1B visas earn higher

base salaries than citizens due to their greater willingness to relocate, but this year the

median salaries at that level were the same indicating that uncertainty and delays

around visa transfers may be having an effect on visa candidates’ ability increase

salary by changing jobs (see Figure 17 on page 27).

Data Scientists

33% of Data Scientists sampled are non-U.S. citizens with permanent residency or an

F-1/OPT, H-1B, or another visa which allows them to work in the U.S. (see Figure 14 on

page 25).

For Data Scientists, 44% of level 1 individual contributors sampled have a visa or

permanent residency. This percentage decreases to 20% for level 3 managers (see

Figure 16 on page 26).

For data scientists at the beginning of their careers (individual contributor level 1),

candidates on a visa earned lower median base salaries than citizens or permanent

residents (see Figure 18 on page 28). This is likely due to the uncertainty and delays

around visa transfers making it harder for visa candidates’ ability increase salary by

changing jobs.


0%

10%

20%

30%

40%

50%

60%

70%

Citizen Perm.

Resident

H-1B F-1/OPT Other

Figure 14 Distribution of Data Science & Predictive Analytics Professionals by Residency Status

Pre

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Da

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73%

67%

13%

19%

10%

9%

3%

1%

4%

1%


0% 20% 40% 60% 80% 100%

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3

U.S. Citizen Perm. Resident H-1B F-1/OPT Other Visa

Figure 15 Distribution of Predictive Analytics Professionals by Residency Status & Job Level

Manager

Level 3

Manager

Level 2

0% 20% 40% 60% 80% 100%

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3

U.S. Citizen Perm. Resident H-1B F-1/OPT Other Visa

Figure 16 Distribution of Data Scientists by Residency Status & Job Level

Manager

Level 3

Manager

Level 2

Manager

Level 1

Ind. Contributor

Level 3

Ind. Contributor

Level 2

Ind. Contributor

Level 1

Manager

Level 1

Ind. Contributor

Level 3

Ind. Contributor

Level 2

Ind. Contributor

Level 1


Job Level Residency Base Salary

Median Difference

from Citizen

Individual

Contributor

Level 1

Citizen $80,000 -

Perm. Resident $81,000 +1%

H-1B $80,000 0%

F-1/OPT $80,000 0%

Individual

Contributor

Level 2

Citizen $95,000 -


H-1B $105,000 +11%

F-1/OPT $95,000 0%

Individual

Contributor

Level 3

Citizen $132,000 -


H-1B $130,000 -2%

F-1/OPT - -

Manager

Level 1

Citizen $130,000 -


H-1B $120,000 -8%

Manager

Level 2

Citizen $178,500 -


H-1B $175,000 -2%

Manager

Level 3

Citizen $250,000 -

Perm. Resident $235,000 -7%

Figure 17 Median Base Salary of PAPs by Job Category & Residency Status


Job Level Residency Base Salary

Median Difference

from Citizen

Individual

Contributor

Level 1

Citizen $98,500 -


H-1B $90,000 -9%

F-1/OPT $92,500 -6%

Individual

Contributor

Level 2

Citizen $135,500 -


H-1B $120,000 -11%

Individual

Contributor

Level 3

Citizen $170,000 -


H-1B - -

Manager

Level 1

Citizen $147,000 -


Manager

Level 2

Citizen $190,000 -


Manager

Level 3

Citizen $250,000 -

Perm. Resident $235,000 -6%

Figure 18 Median Base Salary of Data Scientists by Job Category & Residency Status


Demographics & Compensation | Region


Salaries vary by geographic region. At most job levels, PAPs employed on the West

Coast earn the highest median base salaries (see Figure 19 on page 30 and Figure 21

on page 31).

Data Scientists

Data scientists employed on the West Coast earned the highest median base salaries

across all levels reported (see Figure 20 on page 30 and Figure 22 on page 31).


Job Level Region Base Salary

25% Median Mean 75%

Individual

Contributor

Level 1

Northeast $70,000 $80,000 $81,600 $90,000

Southeast $74,250 $79,250 $79,333 $85,000

Midwest $70,000 $80,000 $79,839 $87,750

Mountain $70,500 $75,000 $78,132 $85,000

West Coast $80,000 $90,000 $90,756 $102,000

Individual

Contributor

Level 2

Northeast $88,750 $100,000 $101,742 $115,000

Southeast $90,000 $97,500 $100,318 $105,000

Midwest $88,550 $95,000 $100,291 $110,750

Mountain $82,000 $95,000 $95,997 $107,000

West Coast $90,000 $99,000 $102,240 $117,500

Individual

Contributor

Level 3

Northeast $120,000 $145,000 $151,033 $167,500

Southeast $130,000 $140,000 $142,927 $153,000

Midwest $115,000 $125,000 $129,300 $140,000

Mountain $113,750 $133,000 $133,237 $146,000

West Coast $122,250 $139,000 $149,439 $160,500


25% Median Mean 75%

Individual

Contributor

Level 1

Northeast $87,000 $95,000 $98,190 $115,000

Middle U.S. $80,000 $87,000 $94,000 $100,000

West Coast $90,000 $100,000 $106,380 $122,500

Individual

Contributor

Level 2

Northeast $120,000 $135,000 $131,971 $142,500

Middle U.S. $111,750 $122,500 $127,990 $142,750

West Coast $132,500 $149,000 $151,289 $170,000

Individual

Contributor

Level 3

Northeast $150,000 $170,000 $175,640 $200,000

Middle U.S. $145,000 $155,000 $160,278 $178,750

West Coast $158,750 $177,600 $180,875 $205,000


& Region

Figure 20 Distribution of Base Salaries of Data Science Individual Contributors by Job Level &

Region



25% Median Mean 75%

Manager

Level 1

Northeast $117,000 $130,000 $130,819 $145,000

Southeast $120,000 $130,000 $131,324 $143,000

Midwest $120,000 $130,000 $132,569 $148,700

Mountain $123,500 $142,000 $137,698 $150,000

West Coast $135,500 $150,000 $149,136 $160,750

Manager

Level 2

Northeast $167,000 $182,500 $183,071 $200,000

Southeast $155,250 $174,000 $170,748 $185,000

Midwest $155,000 $170,000 $175,020 $190,000

Mountain $169,646 $190,000 $188,365 $200,000

West Coast $175,000 $192,000 $197,260 $214,500

Manager

Level 3

Northeast $230,000 $250,000 $265,770 $300,000

Southeast $210,000 $230,000 $253,966 $255,000

Midwest $220,000 $240,000 $250,255 $273,500

Mountain $229,000 $250,000 $249,727 $280,000

West Coast $225,500 $252,500 $262,400 $285,000


25% Median Mean 75%

Manager

Level 1

Northeast $135,000 $146,000 $150,091 $158,750

Middle U.S. $130,000 $145,200 $144,056 $155,000

West Coast* - - - -

Manager

Level 2

Northeast $170,000 $180,000 $185,952 $202,000

Middle U.S. $168,750 $194,500 $191,778 $203,750

West Coast $182,250 $201,500 $198,100 $220,000

Manager

Level 3

Northeast $250,000 $250,000 $280,231 $300,000

Middle U.S. $207,500 $240,000 $237,891 $255,000

West Coast $252,000 $262,500 $276,625 $287,500

Figure 21 Distribution of Base Salaries of Predictive Analytics Managers by Job Level & Region

Figure 22 Distribution of Base Salaries of Data Science Managers by Job Level & Region

*Sample size too small to report.


Demographics & Compensation | Industry


The largest segment of PAPs are employed in the financial services industry (24%)

followed by advertising/marketing (18%). (See Figure 23 on page 33.)

While financial services and advertising/marketing services are still the top two

industries (comprising a total of 42% of the sample), these companies continue to

comprise a smaller percentage of the market compared to previous years (in 2015,

the combined percentage for these groups was 57% and the number has decreased

every year since). This shows a continued industry diversification in analytics.

Figures 24 and 25 (on pages 34 and 35 respectively) show how salaries for predictive

analytics professionals vary by industry.

Data Scientists

The tech industry continues as the largest segment of the data science market with

nearly half (48%) of data scientists employed in tech/telecom/gaming (see Figure 23

on page 33).

Industry variations between data scientists and other predictive analytics

professionals show a clear demographic difference among the two groups.

Salaries by industry were not analyzed for the data science segment as sample sizes

for industries outside of tech were too small at most job levels.


0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Tech/Telecom/Gaming

Retail & CPG

Healthcare/Pharma

Financial Services

Corporate-Other

Consulting

Advertising/Marketing

Academia/Government

Figure 23 Distribution of Data Science & Predictive Analytics Professionals by Industry

PAPs

Data Scientists 48%

7%

18%

11%

8%

10%

12%

12%

24%

6%

10%

5%

11%

3%

2%

12%

DS: N = 2,658


Job Level Industry* Base Salary

25% Median Mean 75%


Level 1

Advertising/Marketing $70,000 $75,000 $77,612 $85,000

Consulting $70,000 $82,000 $84,462 $92,000

Financial Services $71,500 $80,000 $81,798 $90,500

Healthcare/Pharma $75,000 $83,000 $83,091 $93,500

Retail & CPG $75,000 $85,000 $82,191 $90,000

Tech/Telecom/Gaming $75,000 $81,500 $85,143 $94,500

Other Corporate $73,750 $80,000 $83,500 $90,000


Level 2


Consulting $88,800 $105,000 $104,484 $118,750



Retail & CPG $95,000 $100,000 $101,618 $117,750


Other Corporate $85,000 $95,000 $96,622 $102,000


Level 3


Consulting $120,000 $150,000 $158,462 $166,000



Retail & CPG $115,500 $122,500 $126,006 $134,250


Other Corporate $115,000 $125,000 $130,801 $145,000


& Industry

*Academia/Government salaries not reported due to insufficient sample size.


Job Level Industry* Base Salary

25% Median Mean 75%

Manager

Level 1


Consulting $123,500 $137,500 $138,072 $159,750



Retail & CPG $124,250 $130,000 $133,450 $145,000


Other Corporate $116,250 $130,000 $130,566 $143,250

Manager

Level 2


Consulting $167,000 $185,000 $184,073 $200,000



Retail & CPG $165,000 $180,000 $182,627 $200,000


Other Corporate $155,000 $175,000 $177,728 $199,000

Manager

Level 3


Consulting $246,250 $275,000 $274,278 $303,750



Retail & CPG $236,500 $275,000 $296,957 $317,500


Other Corporate $222,000 $250,000 $255,895 $278,500

Figure 25 Distribution of Base Salaries of Predictive Analytics Managers by Job Level & Industry

*Academia/Government salaries not reported due to insufficient sample size.


Demographics | Gender


For predictive analytics professionals, this year’s sample was 74% men and 26%

women (see Figure 26 below) showing an increase in women from 2018 where 24% of

the sample was women.

For level 1 individual contributors, the percentage of women increased from last year

(32% in 2018 to 37% in 2019) showing an influx of women at the junior end that may

contribute to an overall increase in women in analytics in future years.

While at most levels women had lower median base salaries than men at the same

level, the differences remain small (see Figure 29 on page 38).

Data Scientists

For data scientists, this year’s sample was 83% men and 17% women (see Figure 26

below) also showing an increase in women from 2018 where 15% of the sample were

women.

Similarly to predictive analytics, level 1 individual contributors showed the largest

percentage of women in data science at 32% (see Figure 28 on page 37).

Sample sizes of women in data science were too small to examine salaries at the

different job levels.

74%

26%

83%

17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Men Women

Figure 26 Distribution of Data Science & Predictive Analytics Professionals by Gender

Pre

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Da

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nc

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DS: N = 2,658


0% 20% 40% 60% 80% 100%

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3

Male Female

0% 20% 40% 60% 80% 100%

IC-1

IC-2

IC-3

MG-1

MG-2

MG-3

Male Female

Figure 27 Distribution of Predictive Analytics Professionals by Gender and Job Level

Manager

Level 3

Manager

Level 2

Manager

Level 1

Ind. Contributor

Level 3

Ind. Contributor

Level 2

Ind. Contributor

Level 1

Figure 28 Distribution of Data Scientists by Gender and Job Level

Manager

Level 3

Manager

Level 2

Manager

Level 1

Ind. Contributor

Level 3

Ind. Contributor

Level 2

Ind. Contributor

Level 1


$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

$180,000

$200,000

$220,000

$240,000

Level 1 Level 2 Level 3 Level 1 Level 2 Level 3

Figure 29 Median Base Salary by Job Category and Gender in Predictive Analytics

Individual Contributors

Managers

MA

LE

FEM

ALE

96%

95%

94%

102%

97%

97%

$20K

$60K

$100K

$140K

$180K

$220K


Demographics | Years of Experience


Over 50% of PAPs sampled have fewer than 10 years of experience (see Figure 30

below). The predictive analytics profession continues to skew younger, due to

increased interest and the visibility of analytics as a career.

Data Scientists

Data science is an even younger discipline than predictive analytics as a whole: 66%

of data science professionals sampled have fewer than 10 years of experience (see

Figure 30 below).

Note: 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, and

shown above is the distribution of Data Scientists and PAPs by years of experience. However, salary

information is not shown here, because salaries are indirectly related to years of experience through job

category.

0%

5%

10%

15%

20%

25%

30%

35%

40%

0-5 6-10 11-15 16-20 21-25 26+

Nu

mb

er

of Pro

fess

ion

als

Years of Experience

Figure 30 Distribution of Data Science & Predictive Analytics Professionals by Years of Experience


Median: 9.0 years

Mean: 11.5 years

Data Science

Median: 9.3 years

Mean: 8.0 years

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SECTION 4

Appendix A:

Study Objective & Design


Study Objective

This report is a follow-up to last year’s reports: The Burtch Works Study: Salaries of Data Scientists

and The Burtch Works Study: Salaries of Predictive Analytics Professionals, which were published

in May 2018 and October 2018 respectively. Its goals are to show (1) current compensation of

PAPs and data scientists and how it varies, and (2) how their compensation has changed since

last year’s report. By continuing to interview large numbers of PAPs and data scientists annually,

Burtch Works can show both short-term and long-term trends in the demographic attributes of

quantitative professionals and their compensation. Additionally, analyzing data scientists and

PAPs side-by-side highlights the distinctions between the groups that affect salary.

Why The Burtch Works Studies Are Unique

The Burtch Works Studies: Salaries of Data Scientists & Predictive Analytics Professionals contain

highly-anticipated salary and demographic data for Data Scientists and other PAPs, and are

unique because:

Burtch Works’ studies focus solely on Data Scientists and PAPs – The study samples

include only professionals who are currently data scientists or PAPs, and exclude

professions that other salary reports may include, such as business intelligence,

operations research, information technology, and consumer insights.

Burtch Works’ studies distinguish between Data Scientists and other PAPs – The study

separates data scientists (who work with unstructured or streaming data) from other

PAPs because of their more specialized skillset. By comparing the two groups, the

study shows how this distinction affects salary.

Burtch Works obtains this data by interviewing Data Scientists and PAPs – Instead of

relying on data provided by human resources departments or from a self-reported

online survey, Burtch Works interviews every professional individually. An important

advantage of the interview process is that Burtch Works recruiters are able to obtain

information about these quantitative professionals that is not usually provided by

human resources departments that may affect their compensation, such as

education and residency status. Additionally, because of their nuanced

understanding of the profession, recruiters are able to obtain corrections or

clarifications when information provided does not seem credible.

Burtch Works’ salary studies show how compensation varies by job level, region,

industry, gender, and education – The sample size is large enough to show

compensation data, collected over the past year, at a granular level. Further long-

term trends are illuminated with each consecutive report.

The Sample

This sample contains 1,840 PAPs and 421 Data Scientists of the over 40,000 quantitative

professionals with whom Burtch Works maintains contact. Burtch Works collected the data for this

study during interviews conducted over the months immediately following the period of interviews

for the 2018 studies, ending in April 2019. Professionals were included in the sample only if (1) they

satisfied Burtch Works’ criteria for PAPs and Data Scientists, and (2) Burtch Works obtained


complete information about that individual’s compensation, demographic, and job

characteristics.

How Changes in Compensation Were Measured

While some of the 1,840 PAPs and 421 Data Scientists in this sample were also in the samples for

our previous studies (published in 2013, 2014, 2015, 2016, 2017, and 2018), others were not.

Therefore, changes in compensation were not measured by differencing current compensation

and compensation reported for the previous study and then taking medians (and other

percentiles) of the differences. Instead, changes were measured by comparing medians (and

other percentiles) of current compensation to those reported in last year’s study.

Identifying Data Science & Predictive Analytics Professionals

PAPs apply sophisticated quantitative skills to very large sets of data describing transactions,

interactions, or other behaviors to discern patterns in those behaviors and to prescribe actions for

their firms. What distinguishes them from other quantitative professionals, for instance traditional

financial analysts or web analytics professionals, is the volume of data with which they work. PAPs

include data scientists, but data scientists are analyzed separately in this report because they

typically operate on very large sets of unstructured data, requiring additional computer science

skills, while traditional/other PAPs work with more structured data. Burtch Works included the

analysis of data scientist compensation side-by-side with other predictive analytics professionals

to highlight the distinction between the two groups.

To identify PAPs, Burtch Works uses these criteria:

1. Educational Background

Predictive Analytics Professionals typically have a degree – usually an advanced

degree (a Master’s or Ph.D.) – in a quantitative discipline such as Applied Mathematics,

Statistics, Economics, or Operations Research. Some professionals with an MBA are also

PAPs if their MBA program had a quantitative emphasis.

Data scientists are even more likely to have an advanced degree, such as a Master’s

or PhD, than other predictive analytics professionals. These degrees are typically in a

quantitative discipline, such as Computer Science, Physics, Engineering, Applied

Mathematics, Statistics, Economics, or Operations Research.

Note: New educational options include data science degree programs, MOOCs

(massive open online courses), and bootcamps which continue to take hold in the

quantitative community. Some professionals from related careers or fields of study

have successfully pivoted into data science and analytics roles through premier

bootcamps and mid-career Master’s programs.

2. Skills

PAPs are proficient users of analytic tools for discerning patterns in data. Also, they can

use one or more tools for operating on large data sets (see criterion 3), such as SAS, R

and/or Python. They may also have some experience with other business and data

science tools.


Data scientists have expert knowledge of statistical and machine learning methods

using tools such as Python and R, with predictive analytics still at the core of the

discipline. Data scientists are usually proficient users of relational databases such as

SQL, Big Data infrastructures like Hadoop and Spark, related tools like Pig and Hive,

cloud computing platforms such as AWS, and languages such as Python, Java, and

Scala (among others). They may also use TensorFlow and deep learning techniques,

signal processing, and visualization.

3. Dataset Size

PAPs: The size of the datasets that PAPs work with are measured in gigabytes or

sometimes larger. These datasets are typically structured.

Data scientists typically work with datasets that are measured in gigabytes or larger

increments, usually too large to be housed in local memory, and may work with

continuously streaming data. These datasets are typically unstructured.

4. Job Responsibilities

PAPs have job responsibilities in the following areas:

Analytical Database Marketing – Studies existing customers using methods such as

customer segmentation, campaign targeting and effectiveness, propensity

modeling, and customer lifetime value analysis.

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.

Geospacial Analytics – Analyzes data and makes recommendations around store

locations or other physical location decisions.

Human Resources Analytics – Analyzes personnel-related business problems such

as talent retention, attrition, compensation, etc.

Marketing Science – Predicts consumer behavior using analytics such as marketing

mix modeling. Analysis can use transaction-, store-, or market-level data.

Survey Statistics – Analyzes the results of structured surveys, conducted using a

sample of a given population, in order to extrapolate the population’s

characteristics using descriptive and inferential statistical methodologies.

Data scientists: Data science specializations may include Natural Language Processing

(NLP), Computer Vision, Internet of Things (IoT), Deep Learning, or other areas where

unstructured or streaming data is prevalent.

Although they may specialize in a specific area, data scientists are typically equipped

to work on every stage of the analytics process which includes:

Data Acquisition – This may involve scraping data, interfacing with APIs, querying

relational and non-relational databases, building ETL pipelines, or defining strategy

in relation to what data to pursue.


Data Cleaning/Transformation – This may involve parsing and aggregating messy,

incomplete, and unstructured data sources to produce datasets that can be used

in analytics and/or predictive modeling.

Analytics – This involves statistical and machine learning-based modeling in order

to understand, describe, or predict patterns in the data.

Prescribing Actions – This involves interpreting analytical results through the lens of

business priorities, and using data-driven insights to inform strategy.

Programming/Automation – In many cases, data scientists are also responsible for

creating libraries and utilities to operationalize or simplify various stages of this

process. Often, they will contribute production-level code for a firm’s data

products.

Professionals whose jobs are described as business intelligence, marketing research, and

operations research are not considered PAPs, because they do not work with large datasets or

because, in the case of operations researchers, their function is to optimize well described

processes rather than search for patterns in data. Although data scientists are a subset of PAPs,

they were analyzed separately from the PAPs sample because they have atypical computer

science skills to manage unstructured data, resulting in higher compensation bands.

Completeness & Age of Data

A predictive analytics professional or data scientist is included in the sample only if Burtch Works

has complete data about their compensation, and demographic and job characteristics.

All of the 1,840 PAPs and 421 Data Scientists in the sample were interviewed over the months

immediately following the period of interviews for the 2018 studies, ending in April 2019. All were

interviewed by Burtch Works recruiters while executing searches for clients.


Data Science & Predictive Analytics Segmentation

To examine how the compensation of data scientists and PAPs varies, Burtch Works used

characteristics of their jobs (level, location of employer, industry) and demographic

characteristics (gender, years of experience, residency status) to segment data scientists. Burtch

Works developed the following job categories:

Individual Contributors

Level Responsibility Typical 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 Responsibility Typical No.

of Reports

Level 1 Tactical manager who leads a small

group 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)


Burtch Works divided the U.S. into these five regions:

Northeast

Southeast

Midwest

Mountain

West Coast

The firms for which data scientists and PAPs work were divided into these eight industry categories:

Academia/Government

Advertising/Marketing Services

Consulting

Financial Services

Healthcare/Pharmaceuticals

Retail & Consumer Packaged Goods

(CPG)

Technology/Telecom/Gaming

Other

Each data scientist and PAP was assigned to one of these five residency status categories:

U.S. Citizen

Permanent Resident

H-1B

F-1/OPT

Other

Finally, each data scientist and PAP was assigned to one of these three education categories (all

of the professionals in this year’s sample held a college degree):

Bachelor’s degree

Master’s degree

PhD

WEST

COAST

MOUNTAIN

MIDWEST

SOUTHEAST

NORTHEAST

Figure 31 U.S. Geographic Regions


SECTION 5

Appendix B:

Glossary


Glossary of Terms

This section provides definitions of terms used in this report.

Base Salary. An individual’s gross annual wages, excluding variable or one-time compensation such as

relocation assistance, sign-on bonuses, bonuses, and long-term incentive plan compensation.

Data Scientist. A specialized predictive analytics professional who has both the programming proficiency

required to make enormous sets of unstructured data accessible and also the analytical skills for deriving

useful information from those data.

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 their 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 31 on page

46.

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 with an H-

1B visa quits or loses their 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 Data Scientists and PAPs 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; 0-3 years’

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: Considered an analytics Subject Matter Expert; mentors and trains other analysts; 9+ years’

experience

Industry. One of eight groups of firms employing most data professionals. These eight industry categories are

Academia/Government, Advertising/Marketing Services, Consulting, Financial Services,

Healthcare/Pharmaceuticals, Retail & Consumer Packaged Goods (CPG), Technology/Telecom/Gaming,

and Other.

Academia/Government: Institutions whose purpose is the pursuit of education or academic

research such as public universities, private colleges, and for-profit education companies; or

organizations that are a part of the governmental system, such as the Department of Defense and

national research laboratories

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.

Consulting: Industry that includes both large corporations and small “boutique” firms that provide

professional advice to the managers of other firms.

Financial Services: Firms that provide money management, lending, or risk management services,

including banks, insurance companies, and credit card organizations.

Healthcare/Pharmaceuticals: Firms that provide healthcare services, such as hospitals, and firms that

manufacture medicinal drugs.

Retail & Consumer Packaged Goods (CPG): Organizations that purchase goods from a

manufacturer to be sold for profit to the end-consumer, and companies whose products are sold

quickly and at relatively low cost, including non-durable goods (e.g. groceries, toiletries) and lower

quality consumer electronics.


Technology/Telecom/Gaming: Firms that create or distribute technology products or services, such

as computer manufacturers and software publishers, and firms that provide telecommunications

services.

Other: Companies whose industry falls outside of the categories described above, such as airline

companies, distribution firms, media, and entertainment.

Manager. An employee who manages the work of other employees. Managers among the Data Scientists

and PAPs 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 the 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.

Predictive Analytics Professionals. Individuals who can apply sophisticated quantitative skills to data

describing transactions, interactions, or other behaviors to derive insights and prescribe actions. They are

distinguished from the “quants” of the past by the sheer quantity of data on which they operate, an

abundance made possible by new opportunities for measuring behaviors and advances in technologies for

the storage and retrieval of data.

Programming. The process of developing and implementing various sets of instructions to enable a computer

to do a certain task. For the purposes of this study, programming refers to the use of general purpose

programming/scripting languages such as Python, Java, C, C++, or others.

Salary Study. A study conducted to measure the salary distributions 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 or through online surveys, rather than by

interviewing those employees themselves.


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The Burtch Works Study...6 The Burtch Works Study Salaries of Data Scientists & Predictive Analytics...

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