Flash Survey Results for Analytics Professionals!

As you probably already know, I’m always interested in gathering information from and about the analytic community. Our salary study for Big Data professionals is the biggest project we’ve taken on within this mission, but recently we conducted a very brief “flash survey” of our connections regarding a topic that I’m hearing about more and more: individuals’ experiences with being contacted by recruiters via LinkedIn. As a recruiter, I use the site quite a bit, and our staff has participated in a number of training sessions made available by LinkedIn. In these sessions, it’s frequently quoted that an overwhelming 95% of LinkedIn members say they are open to receiving messages from recruiters on LinkedIn.

In this brief survey, I asked our quantitative network three simple questions:

1. How often are you contacted about job opportunities through LinkedIn?

2. How frequently do you respond?

3. Are you actively considering a job change?

We received a great amount of feedback from this quick poll, and in the end we analyzed the responses of 481 analytics professionals. Using information from our database, we were able to categorize these responses further based on geographic region, and career level, using the same hierarchy of levels that we used in the Burtch Works Study. As a reminder, here is how we define job levels and regions: 

Individual Contributors (IC) are professionals without people management responsibility.

• Level 1: responsible for learning the job and being hands-on with analytics (typically 1-3 years’ experience)
• Level 2: hands-on with data, working with more advanced problems/models (typically 4-8 years’ experience)
• Level 3: considered an analytics Subject Matter Expert (typically 9+ years’ experience

Managers(MG) are professionals whose responsibilities include people management.

• Level 1: tactical, leading a small group within a function (typically 1-3 reports)
• Level 2: leads a function and executes strategy (typically 4-9 reports)
• Level 3: member of senior management who determines strategy (typically 10+ reports)

    We uncovered a couple interesting findings, so let’s take a look at what the data revealed.

   #1: How often are you contacted about job opportunities through LinkedIn? 

Key Insights

• More than 25% of analytics professionals are contacted at least weekly, with an additional 63% reporting that they are reached out to at least monthly.
• The most senior level candidates (MG, level 3) reported being contacted most frequently, with 60% saying they are reached out to at least weekly.
• Overall, managers are reached out to more than individual contributors with an average of 42% of managers reached out to at least weekly, compared to only 21% for individual contributors. We have two hypotheses for why this may be: 

o   Many organizations look to put key leaders in place prior to hiring the core staff that will be doing a lot of the analytics. It may be that managers are being targeted now, and as those positions fill, the emphasis will trickle down to the individual contributors.

o   Job titles for management are much more consistent (i.e. Manager, Director, VP) than those of individual contributors, who notoriously have a multitude of titles (e.g. Analyst, Specialist, Scientist, Statistician, Consultant). This means sourcing by titles is more challenging at the individual contributor level.

• Not surprisingly, analytics people on the West coast are contacted most frequently, with 93% of west coasters approached at least monthly.
• People in the Northeast and Midwest were approached in similar amounts – roughly 89% are contacted at least monthly.

#2: How frequently do you respond?

 Key Insights

• More than 50% of quantitative professionals report responding to recruiters’ message on LinkedIn almost all of the time or always.
• Tactical 50% of quantitative professionals report responding to recruiters’ message on LinkedIn almost all of the time or always.
• Individual contributors and managers, however, have very similar response rates overall.
• West Coasters are the most likely to get back to their suitors on LinkedIn: 64% reply almost all of the time or always.
• People living in the Mountain region are least likely to respond, with only 38% replying almost always and 53% replying half of the time.
• Quants in the Northeast were fairly likely to respond (53% almost all of the time or always, 40% at least half the time), showing similar response rates as Midwesterners.

#3: Are you actively considering a job change?

 Key Insights

• 69% of Quants are at least willing to entertain the idea of a job change, or are actively looking.
• Even though 30% of respondents aren’t considering a change, many respond to messages anyway given that only 7% said they never answer these messages.

Conclusions

Over the years, LinkedIn has not only become a powerful networking tool, but also a key recruiting tool. Professionals from almost every industry have flocked to the site, set up a boiled down (or not) version of their resume as their profile, and used it to connect with colleagues, research potential employers, read business news, and search and apply for jobs. I keep finding myself asking, though: is LinkedIn too much of a good thing?

Recruiting firms like ours have leveraged LinkedIn for years in order to target specific individuals for specific roles for our clients. Corporations are adopting the same technique, except (in my experience at least) without the amount of rigor necessary to zero in on the proper candidates. Corporations are hiring sourcing teams to scour LinkedIn for talent, resulting in mass messaging to members who may or may not be relevant for the role they have open.

As we found in our flash survey, the rapidly escalating demand for analytic talent means over 60% of you are being contacted at least monthly via LinkedIn. This is generally a wonderful trend for our profession, but my fear is that “recruiter fatigue” may set in. Some of you may adopt a ‘delete all’ policy, scrub your LinkedIn exposure or even remove your profile altogether. This would be an unfortunate consequence of your increased visibility, but I fear this is where we may be headed. My advice is to know what your goals are and prioritize your options accordingly.  And of course, to stay in touch with your favorite recruiter!

Your Webinar Questions Answered Part 2

Thank you again to those of you who attended my webinar presentation on The Burtch Works Study - a comprehensive look at the salaries and demographics of Big Data professionals. In case you missed it, The Burtch Works Study and webinar presentation are available here. 

Due to the volume of questions I received during the webinar (we had over 750 registrants) I decided to share some of my insights on my blog in two parts, Part 1 is available here.

10.) Are you seeing salaries for non-Big Data (e.g., market research, smaller scale database or CRM analysts) increasing as a side effect of Big Data demand?

LB:  Not yet, but we’ll keep an eye out since we cover those areas too.

11.) I am working in a company that is just beginning to realize the benefits of big data; any suggestions for how to further present the benefits of data and Big Data professionals to a company that so far has not thought it was worth the cost? (Especially how to negotiate salary for data professionals.)

LB: Great question! The payoff for investing in the right Big Data professional is the difference between an ineffective, costly foray into Big Data and a successful and brand-strengthening opportunity. I would highly recommend investigating and presenting specific insights related to your industry. There are new examples every day of companies who are using this wealth of information to their advantage. I would also emphasize that soon the time to gain an edge on the competition will have passed and your company may find itself trying to catch up instead of being a front-runner in the industry. Be diligent about staying abreast of new developments and make sure to network. When you are given the chance, tackle a small project first, rather than going after a big, lengthy project that might not show any results for months. Choose a smaller project where you can quickly get some encouraging reports  that will help to show  that this is worth investing more time (and money!) in, and allow you more time to go after a bigger, more influential project. As for salary negotiations, a couple of my contacts have already used excerpts from our study as support for the case within their organizations for higher salaries within analytics.

12.) When it comes to recruiting candidates, does age play a role as gender does?

LB: In my opinion it has more to do with attitude, energy, commitment and the ability to change and grow. Companies want to hire someone who is capable of adapting to the changes happening every day in the industry and who will be able to capitalize on new opportunities.

13.) Story Telling is a big part of Analytics in any organization. It is an art and a science...Can a Data Scientist play this role?

LB:  Absolutely. Story-telling and strong communication skills are critical for success in analytics. The most successful people in this role can work in a quantitative, technical way as well as selling their ideas to a non-technical audience. I wouldn’t say that one quality is necessarily better to have than the other, but having both makes you a very strong candidate.

14.) There seems to be a lot of tension within and around corporations these days with regards to where the data resides and where it should reside. The data belongs to the company but within the company where does it belong? And if the bulk of a company’s customer-centric data is moved out of IT does this mean the people who work in IT are less valuable or expendable?

LB: You're right in that there's tension in the industry around this issue, mainly because it's become front and center. From my perspective, I think it's extremely important that the relationship between the head of IT and the head of analytics is a strong one. If data is moved out of IT I don’t think that makes IT people more expendable.  Anyone that works for a corporation is expendable, and the best way to manage this is to ensure that your skills are up-to-date and marketable in the long run.

15.) Why is it never a good idea to accept a counter offer?  I understand that a few people would be annoyed, but isn't that a common outcome?

LB: The number one reason people make the decision to leave is because they want a new challenge or a new environment. Money sometimes plays a part but should not be the only reason. If money is the number one reason you should talk to your boss before going on the market because accepting counter offers is generally frowned upon. It’s a small world and the last thing you want to do is burn any bridges during a transition.

16.)  Many of the Analytics Management roles I see posted require deep SAS skills, but do you see growth in roles for those who understand analytics and the applications to solve business problems? Ideally managing a team of analytic professionals but focusing more on strategic goals rather than modeling and being an analytic coach?

LB:  I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

17.)  I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

LB: I would advise identifying what specifically is making you unhappy so you can possibly come to a resolution with your boss. If a satisfying compromise can’t be made, then perhaps it’s time to update your LinkedIn profile and start looking!

18.)  I’ve established myself as a strong candidate and am receiving offers, but I want to make sure that I land the ideal fit: I don't want to get locked into a job that will exhaust me and my family!

LB:  It’s a great question because it’s a difficult one, especially when you are in demand! Considering your desire for work/life balance, as general rule avoid consulting, finance or startups and make sure to discuss your concern with your potential hiring manager to get a feel for the company culture. Feel free to reach out us if we can help you evaluate a role, even if it’s not through us!

At Burtch Works we are always looking for trends within the analytics field, so be sure to check back and see if we have posted any new research results!

Your Webinar Questions Answered Part 1

As many of you know, here at Burtch Works we have been hard at work on The Burtch Works Study - a comprehensive look at the salaries of Big Data professionals. Indicative of the previous scarcity of this information, it has been downloaded over 850 times to date. About a month ago I presented the study via webinar and I am so glad that so many of you could attend! In case you missed it, both the study and the webinar are available for on our website.

 Due to the volume of questions (we had 750 registrants) I was unable to answer them all during the Q&A session so I wanted to share a few more insights on some of the other questions I received. I will be sharing these in two parts, so make sure to check back to see the rest of them posted next week.

1.) What would you recommend for a recent graduate who is hoping to become a Big Data professional? What are the most important skills to have?

LB: Securing a statistics or mathematics degree is crucial to becoming a Big Data professional. Since employers know you won’t have a lot of work place experience try to get as much experience with real, messy data sets as you can. Internships offer a great chance to test your skills and can also offer great references on your working capabilities.  They can help you figure out what you like! Some computer and government agencies have started to open their data stores, which is a great opportunity for students or beginners to practice.  Kaggle offers the chance to compete at solving challenges with real-life data sets, some of them specifically aimed at entry-level job candidates. The more rigorous your quantitative training, the better prepared you will be for the challenges ahead.

2.)  Considering the nature of the job, I think skills should play a greater role than X years workplace experience. How are assessments made regarding skill sets?

LB:  Skill sets are certainly important in the hiring consideration. Savvy with analytics and Big Data tools such as SAS, R, Hadoop etc. is continuing to be important for analytics professionals at all levels. For junior level candidates you must be able to code and tackle big challenges with these tools. For senior level professionals it is important that you maintain a strong knowledge of these tools so that you can not only mentor your team, but jump in when deadlines are tight (which is becoming increasingly true). There is no magic formula for hiring, but I agree that depth of knowledge is important and discounting a candidate solely based on the years experience criteria is a misguided approach.

3.)  I am not sure this is accurate that the bulk of the data scientist talent pool is in west coast. Boston is a huge incubator for Data Scientists.

LB:  There is definitely a large pool of what I would call “Data Scientists” working on the west coast with firms who have access to continuous streams of data. You’re right though, that there are other pockets of professionals in other regions including Boston. Firms in Boston tend to focus more on science, insurance and healthcare related industries. Data scientists have been cropping up everywhere – such as Chicago, New York, Dallas, Minneapolis – since the need for them is no longer limited to Silicon Valley.

4.)  I would love to know if you have a general cost associated with sponsoring a candidate that needs a Visa transfer. I routinely ask our legal team but they resist sharing the expense with me.  It's difficult for me fight for a candidate that is worth the investment when I don't know what the investment is. And certainly my own expectations of a candidate would also be very different if the cost is $2k versus $15k.

LB:  From what I’ve been told, the ballpark cost of sponsoring a visa is between $6k and $10k. I know it's not cheap ($2k) nor extremely expensive ($25k+).  I covered some information about the OPT/H-1B process in previous a blog post as well as more about the green card process in another blog post. For more about the residency status of quantitative analytics professionals, see this blog post.

5.) Why does the Retail industry pay so low for IC level 2?

LB:  It's an interesting question, and I'm not sure that I have an exact answer. This trend holds, though, not only in our study, but also in my experience recruiting for retailers. Generally, retail as an industry is notorious for being extremely tight with expenses due to the very small profit margins. For analytics in particular however, this inclination may be hurting retailers who are trying to compete for Big Data professionals with more competitive tech firms like Amazon and Netflix.

6.)   Did you find that salary is related to the name of the university as well? If you graduated from a top 10 graduate school will your salary be higher?

LB:  Not necessarily. Although a degree from a big-name school may boost your salary right after you graduate, the effect diminishes over time as your career success becomes the most important indicator for how your company should compensate you. Also if you didn’t graduate from a top school but were successful at a rigorous, quantitative internship that can definitely tip the scales in your favor!

7.) You mentioned that the higher salaries in the Northeast and West Coast don't come close to covering the higher cost of living there.  When candidates take new positions and move to these regions are they accepting small pay increases or increases that will cover the higher cost of living? Thank you!

LB:  In my experience, we see an average salary increase of 14% across the US and sometimes just above that for individuals in the Northeast and West Coast. We just rarely see a substantial increase, even though quant professionals are often moving from an area of lower to higher cost of living. However Big Data professionals will each have their own ideals when it comes to industry, work environment, compensation and degree of challenge at their job. Money is not the only factor to consider when evaluating a career move.

8.)  Is it fair to draw the connection between job descriptions (going down from Data Scientist to Insights Manager) and the levels of IC 1-3 and Mgt 1-3? i.e., are they linked closely enough to assume Data Scientist is IC Level 1?

LB: Very good point! That is why we kept Data Scientists and Market Research individuals i.e. Insights Managers out of the salary pool because they tend to be substantially different than the quantitative professionals. This helped achieve a consistency in results across all levels.

9.)  Since the Big Data field is relatively new, how are salaries bench-marked to know what is the right salary to expect for a role? Have your salary survey results been compared to Information Week's annual survey of IT pros or with the self reported numbers on glassdoor.com?

LB:  Salary surveys are common in other areas like IT (with reports readily available) but analytics professionals are very different therefore it would be inaccurate to directly compare the two. Glassdoor is also a good resource if you're interested in self-reported salaries from people working at specific companies.

Immigration Reform and Analytics

Immigration reform has been on the lips of almost every political commentator in the past few weeks. The topic is heavily debated and cumbersome in detail, but as members of the analytic community we must understand it as it affects our job market directly.  As we reported in The Burtch Works Study 58.8% of entry-level Big Data professionals are not U.S. citizens, which means that companies are looking abroad for professionals with the necessary skills to tackle their data sets and analytics challenges.

Companies want the best and brightest in the quantitative sciences, and the fact is that right now many of these workers come from outside the U.S. As pointed out by John Shinal in this article that I posted on twitter many companies are building offices overseas when the available visas run out since, “the 65,000 cap on H-1Bs for this year was reached on the very first day that the government began accepting visa applications”. The competition is quite clearly global, and this immigration reform could help boost our standing in the community by encouraging companies to keep their offices here in the U.S.

Rosario Marin, former US Treasurer under President George W. Bush points out in her recent op-ed piece in the Wall Street Journal, “The current skilled-labor shortage—particularly for workers in science, technology, engineering and math occupations—puts U.S. companies at a disadvantage. By 2020, an estimated 1.5 million jobs will go unfilled, according to McKinsey & Co. Until America can educate enough graduates in these fields to meet the demand, legal immigration is the only option to find the necessary talent.” Most of these 1.5 million jobs are in analytics and that number will only continue to grow with further advancements in harnessing Big Data. Even most of the graduate students who excel in quantitative programs at the Master’s and PhD levels in the US come from outside of America. When these students graduate, we need to ensure companies are able to sponsor their visas and keep them in the country lest they find employment opportunities in Europe or Asia.

My hope for any change in the current immigration system is that the United States can continue to provide the best technological and scientific advancements that benefit us every day. Relative to that, as the economy continues its recovery we will see a renewed urgency to hire and fill positions that support such advancements within analytics that didn’t exist ten years ago. This Gallup Poll shows Americans mostly in favor of some type of immigration reform, and though I would not claim that the current bill is the best option, it is certainly a starting point. 

Data Scientists . . . or Data Wannabes?

Ladies and gentlemen of the analytic community – we have an epidemic. A seed has been planted in the minds of quantitative professionals that has sprouted and will not subside. The more we hear about Big Data, the more we hear about ‘Data Scientists.’ And like with any hot job, once the media starts to speculate on salary, candidates go nuts. As the President of Teradata Scott Gnau said in a recent article about careers in Big Data, “There are a lot of people who can spell Hadoop and put it on their resume and call themselves data scientists.”

Gnau makes a plea to get the term ‘Data Scientist’ defined and I could not agree more. When a national article makes the claim that one of these workers can be making $200K+ with a couple of years of experience, you can bet everybody who works with data will start calling themselves a Data Scientist. I have already seen it start to happen and thought I would offer my two cents on the reality of this highly coveted (and grossly overused) title.

The compensation issue is a tough one to tackle. Probably the most common question I get asked as a recruiter, by both candidates and clients, is related to salary. While it’s true that careers in marketing analytics are in demand and lucrative, this is all dependent on multiple factors including: years of experience; software familiarity; advanced degrees; pedigree of school; location preferred; and many more.  So when Rob Bearden, the CEO of Hortonworks, says that a “qualified data analyst” coming right out of school can make $125K, note that this is a very specific candidate with a very impressive background. To name just a few bullet points of the perfect candidate, data scientists typically have: 

• A PhD in Computer Science and advanced degrees in other highly quantitative disciplines like Mathematics  
• Knowledge of MapReduce tools like Hadoop
• Use of tools such as Python, R, Java, Hive, Pig
• Prior work or internship experience working with enormous, unstructured data sets

These are just general guidelines, of course. To be sure, a candidate with a Masters in Statistics can be a Data Scientist but this candidate will likely not be demanding as high of a salary as someone with two PhDs and Google on his resume. It might help to think of Data Scientists as a unique subset of the Big Data analytics profession. Just as an IT professional may work with Big Data, a marketing analyst who builds predictive models is not necessarily a Data Scientist.

Allow me to reiterate what I’m sure you have already heard countless times in the media: careers in Big Data are in demand and continue to grow. However, not every person with a quantitative background or experience in analytics is a real Data Scientist. The tools they are using are very new and many companies looking to take advantage of Big Data do not have a previously established foundation of Data Scientists to provide road maps for less experienced professionals. Likewise, innovative companies like Hortonworks that work to support tools such as Hive and HBase can and must offer workers competitive salaries to ensure the use of these systems are further developed.

For now we will continue to see ‘Data Scientist’ placed on resumes to catch the attention of hiring managers, and hiring managers will continue to shell out unprecedented resources to attract talent. Rightfully so – these guys are one in a million.  

Remembering an Inspirational Teacher

As most people in the quantitative job field would agree, math education in the United States has been mediocre at best over the last few years. Earlier this month, John Holdren, the Director of the White House Office of Science and Technology Policy (OSTP) reiterated the steps that the Obama administration is taking to improve test scores in math and science and further detailed plans to set aside money to overhaul math education.  My pessimistic side can’t help but think this is too little too late, but at least the President is acknowledging that we have a problem that must be addressed. I have made my opinion well known about this issue and with three teenagers at home, the topic is one that affects me not only as a recruiter but as a mother.  

On a personal note, I am filled with excitement and pride as I watch my two oldest children, Jay and Becky, graduate high school this year and embark on a new journey at college. As parents, we have to step aside in moments like these and hope that our children have made the right choices and will choose the best paths for their futures. Looking back on the opportunities that they have had in school, I am most struck by the fantastic efforts of Sandy McDermott who taught math to both Jay and Becky in middle school. Her life was sadly cut short in 2008 and a scholarship was set up in her memory that asks students what math education means to them. As I read my son’s essay honoring Ms. McDermott, there isn’t a doubt in my mind that if there were more teachers like her in the world instilling a passion for math from an early age, we wouldn’t have the problems in our education system that plague so many students and schools.

Ms. McDermott was such a lovely woman and inspirational teacher to hundreds of children and I feel humbled to share just a glimpse of the impact she had on her students with my son’s essay.

When I was a fifth grade student at Dewey Elementary School, I took an algebra course taught by Ms. McDermott at Nichols Middle School.  I had always enjoyed math, but this course was different.  For the first time, I had a hard-cover math text book.  More importantly, I learned to use variables and no longer just did arithmetic with numbers.  I learned to apply math to solve problems.

I remember Ms. McDermott’s response to a question I heard in that class and every other math class I have taken since: “When am I ever going to use this stuff?”  Ms. McDermott’s response was, “Whenever you want to.”

Her response provoked some kids to say, “So you mean never?” or “Okay, whatever.”  However, I got her point.  I silently finished her sentence: “Whenever you want to . . .” to solve a problem.  There are many problems that might seem impossible to solve at first, but if you see the math in the problem – if you turn it into a math problem – it is easy to get to an answer.

I have seen examples of this lesson learned from Ms. McDermott again and again in many classes, not just my math classes, but also other classes, such as physics and economics.  I also apply math to solve problems at home.  My hobby is to write video game programs using languages such as Visual Basic and Java, and I use math to write programs that are efficient.

Because I enjoy using math to solve problems, I am planning to study applied math and computer science in college (at the University of Washington) and then to make it my job.  Thank you, Ms. McDermott.  For me, “whenever you want to” will be every day.

Visa Talk Continued: The Green Card Process

In February, I talked to immigration attorney David Rubman about the steps involved with obtaining and transferring an H-1B visa. The process can be long and while on an H-1, candidates find their options limited as many companies will not sponsor transfers. The end goal for these workers is obviously permanent residency, but just how does one apply for and receive a green card? I continued my talk with Mr. Rubman to find out more.

Green Cards

Linda:     You said in our last conversation that the H-1B cannot be issued for more than 6 years.  What happens after 6 years?

David:    That’s why people apply for a green card.  A green card, also known as Permanent Residence, gives the individual the right to live and work in the US permanently.  It is also the first step to applying for US citizenship.

Linda:     How long into an H-1B can you apply for a green card?

David:    There is no limit on when an employer can apply for a green card for a worker.  It just depends on the employer’s policy.  Most employers want to wait some time to decide if they like the employee enough to sponsor him or her for a green card.  But legally the employer can start the green card process at any time, even before the employee starts working.  It is completely separate from the H-1B process.

Linda: What is the company’s responsibility for sponsoring a green card? You said most employers wait to start this process, presumably because of the cost and time involved?

David: The green card process is more complicated than the H-1B and more expensive.  For one thing, it is a process that requires the employer to advertise for US workers for the position, and then show it can’t find a qualified US employee. 

Linda: As opposed to an H-1B where there is no requirement that the employer show there is a shortage of American workers?

David: Exactly. 

Linda: So how long does the process usually take?

David: It varies based on circumstance. For persons with Master’s degrees born in India or China, the process can take more than 4 years.  For those born in other countries, the process can be completed in about 1 year.  For those without Master’s degrees, it is a longer process. 

Linda: What is the financial cost for employers sponsoring a green card?

David: Costs can be up to $10,000 for a green card case.

I want to thank Mr. Rubman again for the taking the time to provide his expertise. I know that many unique situations arise with both clients and candidates within the visa process, so if you have any additional questions, do not hesitate to reach out to Rubman & Harris.