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.
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