Friday, April 4, 2014

The Great Debate: SAS vs. R

I’ve been recruiting analytics talent for over 30 years, and now over the past few years I have watched open source R seemingly catapult to popularity alongside the proprietary standby SAS. Despite hearing more about R from clients and candidates than ever before, determining whether R was actually more popular than SAS proved difficult. A quick Google search for “R vs. SAS” returns more than a few pages dedicated to each side, as well as several heated LinkedIn discussions relating to the topic, with no definitive answers.


For my latest “flash survey” I wanted to quantify the preferences of the Burtch Works network, and asked one simple question: Which do you prefer to use, R or SAS?


With even more participants responding that couldn’t seem to pick just one or picked neither, this tells me that such a seemingly simple question has a more complex answer. Here are just a few of the entertaining responses we received:

  • “I am a purist, so SAS.”
  • “R - unless you have a ‘both rock’ category – it’s a close one.”
  • “Never learned how to use R. Too damn old.”
  • “R. But isn’t the debate more between R and Python?”
  • “SAS as long as I’m not paying for it.”
  • “SAS. What’s ‘R’? (Joking…)”

Curious as to how these results may vary by factors like industry or years of experience? I know I am! I can’t wait to dig into the data, and in the next few weeks will be posting a full write-up on the blog with our findings. Thanks to all who participated and stay tuned!

To be the among the first to see all our latest job postings, blog posts and news - including our upcoming Data Scientist Salary Study - be sure to follow Burtch Works on LinkedIn.

Wednesday, March 12, 2014

Tips for Hiring Data Scientists

This post is contributed by Frank Lo, an experienced data science professional and friend of Burtch Works. Frank is currently the Head of Data Science at Wayfair, as well as the founder of DataJobs.com.

 There is so much hoopla around the need to hire data scientists -- but amid all the frenzy, I notice a major disjoint between what companies think they need and what they actually need to leverage data science for business value. I come from the background of leading a data science team; on top of cultivating the team and diving into the nitty-gritty of data, I spend a lot of time recruiting, trying to find new rock-star talent. I'd like to share a few things I've learned along the way around what to look for.

  Look for quant experts with business hustle.

 First, let's think about what is data science. It is not only a combination of technical and quantitative disciplines, but also the acumen to leverage STEM skills to transform business. Too often, we focus too much on pure engineering/math ability, kicking business smarts to the wayside. On the contrary, I think business acumen is one of the most important traits of effective data scientists -- so much so that I filter out strong tech/math candidates if they have trouble thinking through the business applications of their quantitative work. Ultimately data scientists create value by being consultants to the business. Data mining and predictive analytics by themselves are not the point, but rather the means to enable intelligent strategy development. Look for people who are good at all of the above, including business.

  The #1 intangible is intellectual curiosity.

 The spirit of data science is discovery. Given a mountain of data, what inferences can we make? What truth is revealed or predicted? The strongest data scientists are motivated by this curiosity to explore data in very creative ways. When I recruit for my own team, I look for people who are not only good at answering questions, but who want to ask their own questions. This genuine inquisitiveness is rocket fuel in driving a data scientist's search for meaningful discoveries in data. It is so critical to the role that we turn away candidates who cannot demonstrate that they are brimming with intellectual curiosity.

 How to screen for this intangible? One interview question I ask everyone: "tell me about a data science project or investigation that you initiated on your own, outside of school and work." I look for candidates who can respond to this at length, diving into quant, tech, or business problems they were so intrigued by that they carved out their own time to pursue. It is a great signal they will thrive in a data science role.

  Don't filter candidates based on degree.

There is a notion out there that the best data scientists are the ones with Ph.D's. From my experience interviewing and evaluating candidates, it is my opinion that academic degree is the last consideration that matters. Some Ph.D candidates have very well-rounded skills and do become top performers. Though, I've found that many others find themselves mentally stuck far down the academic rabbit hole, and have difficulty translating their focused depth into value in a business environment. My point is: degree by itself is a very incomplete indicator, so don't filter candidates on it. Consider all academic backgrounds, zeroing in on well-rounded skills paired? with the right intangibles (i.e. intellectual curiosity, business acuity, etc). You'll find that many star data scientists are self-taught hackers and mathematicians, who may not have wanted to sacrifice work experience for academic credentials, but have a very complete data science knowledge base.

 Clearly, there is a lot to consider in how to hire solid data science talent. The reality is that data science is very multidisciplinary; you're looking for a blend of skills that can reveal itself across a wide range of academic backgrounds and professional experiences. But as long as you have a solid understanding for the nuance in what makes a good data scientist, you'll have an easier time trying to identify the right people for your team.

Keep an eye out for our Data Scientist Salary Study - a follow up to the landmark Burtch Works Study: Salaries of Big Data Professionals - which should be released in the next month.

Monday, March 3, 2014

Flash Survey Results Part 2 – How Do Quants Find Their Jobs?

In my last blog, I posted results from a short “flash survey” of my analytics connections about their motivating factors when changing jobs. Although there are articles about where candidates in general find their jobs, I wanted to see how different those numbers might look for analytics professionals in particular. The second question in my quick survey was:


2.) What was the source of your last job?



Key Insights

  • Not surprisingly, the most likely source for placement is someone within your network, with 36% of respondents having found their position through a referral or networking.
  • The second most likely source is to apply directly with 30% of participants.
  •  Executive recruiters and corporate recruiters account for roughly one-third – 34% combined – of all job sources.


It was not surprising to me that referrals and networking are the most common way that analytics professionals find their jobs. Recruiters show good representation in analytics (34%) when compared to the results from the general candidate population (16%). Working with a specialized recruiter with whom you have developed a strong relationship can be like an extension of your network, and a very well-connected one at that. Especially in such a specialized field, it works to your advantage if your recruiter can speak the same language that you do. It will be interesting to see how these numbers will change in the future.

Monday, February 24, 2014

Flash Survey Results Part 1 – What Motivates Quants to Change Jobs?

We’re midway through the first quarter and the analytics hiring market is heating up – organizations are evaluating their business goals and hiring needs, and candidates are evaluating their career options for the coming year. As a follow up to my blog post with my predictions for the analytics hiring market, I decided to conduct another “flash survey” of my connections to see what motivates them to make a career move and how they found their last job. I’ve read studies about how Quants are most motivated by intellectual curiosity, and was interested to see if this would translate to their career aspirations. I received 216 responses to my quick survey, the first question being:

1.) What would be your top two motivating factors if you were to change jobs?



Key Insights
  • Overall, salary and opportunity for growth were the top two motivators for respondents, with 47% and 44% respectively.
  • Challenging work was third with 38% of respondents choosing this in their top two motivators.
  • A change in company (16%) and industry (11%) were the factors least likely to inspire a job change.


Conclusions

Quantitative candidates are more in demand than ever, and if the results of this poll are any indication the talent pool is ambitious and hard-working. Their focus on salary is interesting, and reflects what I found in the Burtch Works Study last year – that Quants changing jobs can expect a 14% pay increase. When compared to the average 2-4% yearly increase they can expect if they stay at their jobs, it’s no wonder the hiring market is humming with activity and that salary is a top motivator.

Opportunity for growth was a close second, and it seems that analytics professionals are evaluating “promotability” when considering new roles, compared to the opportunities available in their current organization. For my analytics job seekers, I wanted to offer a word of warning here – although you can realize an increase in pay, I usually don’t see candidates substantially improve their title when changing jobs. It’s much more common to see a candidate make a lateral title move when changing companies, knowing that there is a greater possibility for a promotion down the line in their new organization because it is in a growing field or expanding.


Stay tuned for my next blog when I will post the results from my survey’s second question, “What was the source of your last job?” 

Wednesday, January 22, 2014

My Predictions for the 2014 Analytics Hiring Market


Thanks in part to the media coverage of Big Data, analytics has become inescapable. To those who have worked in and around this industry for years this information is nothing new - merely more visible. Every day I’m hearing about more and more businesses warming to the idea that there are actionable insights readily available in their data if they know how to use it.

It is an exciting time to be a statistician, data scientist, or analytics professional, and if you thought 2013 was big, 2014 promises to be even bigger. Without further ado, here are my top predictions for the analytics hiring market:

1.)  Real-time analytics becomes increasingly important to business intelligence – Industries like the gaming industry already give their customers incentives in real-time and retailers are quickly moving toward real-time couponing. Those of you with real-time data experience will be in high-demand for these growing roles.

2.) Data scientists will be embedded in analytics groups – In addition to analysts who can build complex models, there will be an increasing need for data scientists, who with their background in analytics and computer science can wrangle massive, unstructured data sets. Companies hoping to extract the full benefit from their data sets will need a combination of both groups.

3.) If you don’t have digital you will be a dinosaur! - If your skill set doesn’t already include digital analytics your career plan should be to get experience and soon. Without it your skills will become outdated and obsolete.

4.) Sentiment analysis will continue to erode traditional survey methods – Use of social media data and other “free sources” has the opportunity to overtake traditional survey methods, which will impact the marketing research talent landscape.

5.)  Corporations will bring on internal staff to replace high paid consultants- Consultants got the ball rolling, and now companies will seek to take on full time staff.

6.) However, this will not adversely affect the consulting industry – Demand is so high for analytics experts (and continuing to grow) that there will still be companies looking to small and large consulting firms. Despite some companies hiring full time staff, the consulting industry will continue to experience growth. Heavy travel schedules will continue to be the norm for these opportunities.

7.) The trend towards wearables and PEDs will create new positions- If the buzz from CES2014 is to be believed, the wearables trend is increasing. As with any new technology that creates massive amounts of data, analytics positions will be created in the industry to make sense of and capitalize on all that information.

8.) Startups’ grip on the hiring market will loosen- I have spoken with candidates who have worked with many failed startups, and I think that this year the tendency towards hopping from failed startup to failed startup will begin to shake out. Candidates who have been through the failed startup cycle will begin to realize that to build a career it will be important to learn from an established group.

9.) Graduates from the new analytics programs will begin to hit the job market - This is not so much a prediction as it is inevitable, and I am very interested to see how well these new programs have prepared their students to enter the quantitative workforce.

What trends do you think we will see in the analytics hiring market? Let me know what you think in the comments below.
 
 

Thursday, December 19, 2013

Burtch Works' Most Popular Social Media Posts from 2013

2013 was a busy year for Burtch Works’ social media accounts, and I wanted to take the time to revisit some of our most popular links this year. 

Our top links include blog posts on topics from resume writing to data science wannabes, as well as links to our original research, including salary studies and a flash survey of our network. 

Here is our social media year in review:

1.) The consulting trend is really gathering steam but the opportunity is not for everyone, which I addressed in Should You Take That Consulting Role? Here's Why or Why Not.

2.) With networking being more important than ever and holiday parties around the corner these 18 easy conversation starters from Careerealism that I posted were perfectly timed.

3.) Apparently everyone is thinking about retooling their resume, because my latest blog post Need to Rewrite Your Resume? Four Tips Before You Submit is one of the most popular posts on our social media this year.

4.) Are you a real data scientist? Or just a Data Wannabe?


5.) McKinsey put out an informative infographic on Big Data and ROI Big Data Big Profits.

6.) Lou Adler once again published a great article on LinkedIn with 5 Things You Must Not Do In an Interview, and 5 Things You Must.

7.) The Burtch Works Study: Salaries of Big Data Professionals was released in July, and Silicon Angle ran a story about lucrative salaries for foreign-born analysts.

8.) Our Marketing Research Salary Study was released in October, both salary studies and their webinar presentations are available here.

9.) Back in April I addressed the lack of urgency to hire in Help Wanted But Hiring Slow.

10.) While catching up on TEDxTalks in September I watched Big Data, Small World by Dr Kirk Borne which is definitely worth your time. He also maintains a very active twitter presence and was voted the #1 influencer on Big Data by Onalytica.

11.) In a guest post on my blog at the beginning of November, Burtch Works’ entry-level recruiting specialists shared their advice in How to Get Your First Analytics Job.

12.) Researchers at the University of Pennsylvania analyzed social media Big Data to create word clouds showing commonalities by age, gender & personality. Fascinating stuff!

13.) Forbes published my article Five Ways Marketers Can Keep Quants From Quitting in December.

14.) We tallied results from our Flash Survey for analytics professionals about how many are approached via LinkedIn about new job opportunities and how often.

15.) What Design Thinking Can Teach Analytics Professionals was an attention-grabbing article from Data Informed.

With all the attention focused on Big Data, I expect next year to be even busier as more companies look beyond the buzzword and start seeing returns on their investments. The analytics hiring market will continue to heat up, so keep an eye out for my 2014 hiring predictions blog in the beginning of January. For more career advice, blog posts and industry news throughout the year be sure to follow Burtch Works on LinkedIn and Twitter. Happy Holidays everyone!

Monday, December 9, 2013

Need to Rewrite Your Resume? Four Tips Before You Submit

As you probably know, expecting to land an interview without revamping your resume is a costly mistake. Obviously your resume should reflect any changes in your work history, but it should also grab an employer's attention and convince them that you are worth interviewing. Since their first impression of you will likely be the resume you submit, you should make sure to put time and thought into crafting one that tells your story and emphasizes your unique skill set.

In continuation of my guest blogger series, Burtch Works' marketing research specialists Karla Ahern and Naomi Keller will be sharing their top tips for writing a resume. With years of experience viewing many (many) resumes for our candidates, there are four main areas that demand your attention. You can find the original article posted here.

As recruiters and former market researchers, we have a unique vantage point into the field of market research. We have both an intimate, experiential knowledge of the day-to-day life of market research, as well as an overall view that allows us to see the varying trends that affect the marketplace. As a result, our candidates come to us with a variety of questions regarding their search process and “How can I improve my résumé?” is one of the most frequent ones we hear. A strong résumé is compelling and concise, and it effortlessly tells your story. Here are our top tips to achieve that effect:

1. Highlight the impact you’ve had at your organization, not just your day-to-day responsibilities. 
As a market researcher, you already understand the importance of telling a story. It’s not enough to provide data tables for a study; you have to provide insightful analysis and connect it to the overall effect for the brand. The same goes for your own résumé. While your responsibilities are a crucial element of your story, employers are always looking for that last impactful punch, so be sure to explain how you contributed to the success of the company in a quantifiable way.

2. Add summary and objective statements.
Don’t make the mistake of thinking that your work history will tell your story for you. Adding a summary and objective statement to the top of your résumé is your chance to contextualize your experience and craft your career narrative. You want to boil down the essence of your background and phrase it in a compelling way that draws a clear line between your achievements and your career objectives. 

3. Include your core competencies. 
Similar to summary statements, we recommend that candidates include a list of key competencies at the top of their résumés. This is your opportunity to call out your methodological expertise and specialized skills in a visually impactful way. As with any résumé, tailor the highlighted skills to the specific position that you’re seeking. If it’s a consumer insights manager role, for example, you’ll want to highlight the types of survey methodologies that you’re familiar with, quantitative and qualitative research expertise, vendor management experience, etc. 

4. Keep your LinkedIn profile fresh.
Sometimes having a polished résumé only goes so far. There are hiring managers and recruiters cross-checking you on LinkedIn, not to mention sourcing for positions that you may want but don’t even know about. Keeping your profile up to date is imperative. It’s fine to be selective about what you share on LinkedIn, but at the very least, make sure your profile is current to your latest job, consistent with your résumé for all dates and titles, and tightly edited, and includes an overall description of each of your roles.  

Ultimately, the most important thing to remember when crafting your résumé is that the first make-or-break initial scan will be brief, often less than 20 seconds. Your goal should be a concise, organized and memorable résumé that tells your story, both where you’ve been and where you intend to go.