What Does an Aging Workforce Mean for Science?

27. March 2017

A new study conducted by researchers from The Ohio State University, USA, looks at the rise in the average age of scientists and engineers in the STEM field's U.S. workforcea topic that potentially bears both on the country's overall scientific competitiveness and the outlook for younger scientists entering the workforce today. In the study, economists David M. Blau and Bruce A. Weinberg looked in particular at why the workforce is aging rapidly and what that means for science. For instance, they note, "The science and engineering workforce has aged rapidly, both absolutely and relative to the workforce, which is a concern if the large number of older scientists crowds out younger scientists."

The study, published in the Proceedings of the National Academy of Sciences (PNAS) this week, is part of a larger research effort to determine what happens to the creativity and scientists and productivity of scientists as they age. "The conventional wisdom has been that scientists become less creative and innovative as they age," Weinberg said in a press release. However, Weinberg also said that some of his own research suggests otherwise.

Motivating the study—which rested on a combination of data from the U.S. Census Bureau and from a National Science Foundation survey of doctorate recipients conducted between 1993 and 2010—was the rising average of employed scientists in the United States. The average age increased from 45.1 years to 48.6 years between 1993 and 2010, faster than that of the workforce as a whole. And the authors also predict in the paper that the average age will continue to rise, reaching a steady state of 50,9 years in the future, all else being equal.

One reason for concern about an aging scientific workforce is that older, presumably less creative and entrepreneurial scientists might crowd out younger, presumably more innovative and enterprising talent. Weinberg says, however, that those fears have yet to be proven. (Though a press release on the study did admittedly note that the current study can't determine if this is actually happening.) Still, the researchers hope these insights will eventually also help determine if advancing retirement ages are indeed keeping young scientists out of the workforce.

Weinberg says that those fears have yet to be shown as rooted in fact (and, indeed, even Blau and Weinberg's PNAS study doesn't really offer an answer.) Still, the researchers hope the insights in the new paper, coupled with their other work, will eventually also help determine if advancing retirement ages are indeed keeping young scientists out of the workforce.

One reason that the scientific workforce continues to age on the job is that the law in the United States now allows them to do so. A law that had mandated that professors retire at age 70 was repealed in 1994. In the wake of that move, the proportion of scientific workers aged 55 and older ballooned from 18 percent in 1993 to 33 percent in 2010.

This is a significant increase relative to non-scientific fields, for which the proportion of over-55 workers expanded from 15 to 23 percent in the same period. More specifically, the researchers found that a "substantial majority" of the workforce rising in average age is due to the aging of baby boom scientists—of which there is a large cohort in the workforce.

According to the PNAS study, the average age of scientists in nearly all disciplines are on the rise. Even computer and information science—a field historically known for attracting the young and tech-savvy—has, according to a press release, "seen a graying of the workforce." Indeed, almost counterintuitively,  the average age of computer scientists is increasing more rapidly than in other fields.

In additionsomewhat surprisingly, in light of rapid demographic changesthe authors found no correlation or influence on the average age of the scientific workforce with immigration status or gender.

What this rise in average age means for science is yet to be fully understood. The authors  conclude that "The implications of these findings depend on whether and how rapidly scientific productivity declines with age, and whether the life cycle scientific pattern will change in response to the aging scientific workforce." Blau and Weinberg say they'll continue "to investigate the implications of our aging scientific workforce" in their future work.

Women in STEM: A New Report

9. March 2017

In recognition of the 106th International Women’s Day (IWD) on 8 March, countries and organizations have rallied to promote the significance and importance of women worldwide. At the Emirates Airline Festival of Literature in Dubai, United Arab Emirates, a panel of female authors discussed this year’s IWD theme “Be Bold for Change.” The City of Brisbane, Australia, hosted an International Women’s Day Fun Run in support of women with breast cancer. The training company General Assembly presented 11 Women’s Day Lightning Talks around the globe, featuring panels of “female leaders who are spearheading local innovation in tech, culture, social media and politics.”

The scientific-publishing behemoth Elsevier has now released its own lengthy study on the changing roles of women in research. In “Gender in the Global Research Landscape,” a team of Elsevier researchers reviewed data from the company's Scopus and ScienceDirect databases covering two separate five-year periods, 1996-2001 and 2011-2015, comparing authorship of papers from 12 different geographical regions as well as across 27 Scopus topics.

There’s a big caveat in assessing this report: It is not truly comprehensive. In particular, it omits data from China, and thus contributions of female scientists from a region that’s emerged as one of the world’ biggest single producers of scientific research. It also doesn't cover the work of unpublished and unpatented female scientists working in industry.

Still, within the sphere that it does cover, the report reveals some intriguing trends. One key finding, highlighted here, notes that the number of women researchers and inventors publishing papers and filing patents has increased. Another finding shows that research articles authored by women are cited and downloaded at a rate similar to those authored by men—even though women are still publishing fewer papers.

While this study offers some signs of progress toward reducing the gender disparities that plague many STEM fields, and can also be viewed as an extensive and much-needed benchmark of the progress made by women in recent decades, it may best serve as a means of identifying where gaps exist, both in available survey data and in global scientific gender diversity. Obviously, any study will be limited by data availability, and the sheer scope of women in STEM makes assessing progress a challenge. Still, the Elsevier study looks like a useful data point—and it will be interesting to see whether the same data set gives rise to more comprehensive assessments in the future.

Diversity, Women in Science

Coming of Age in the Workplace: Advice for Millennials

6. February 2017

According to the Pew Research Center, "millennials" are those born after 1980, making them the first generation to come of age in the new millennium. In popular media, millennials have not been regarded with such technicality and formality. Instead, the generation has gained notoriety for negative connotations that have been popularized via television and other media platforms, where characters decry millennials as lazy, entitled, tech-obsessed, etc.

Justin Bariso of Inc. Magazine noticed the heavy flack pointed at the age group, and has some advice for those on the receiving end. He states, in this article, that he is "a fan" of millennials, and the while the judgment toward the group may be in many cases be unfair, it's simply (and unfortunately) "the reality we live in." Bariso goes on to say that, "Society doesn't work like the justice system: In the eyes of many, you're guilty until proven innocent."

So for those born after 1980 and wishing to persuade the Baby Boomers in their office or lab that they are nothing like the millennials portrayed by the media (such as those seen on reality TV shows like Jersey Shore), Bariso has a few tips. He advises millennials to be open to criticism, because, inevitably, "you're going to get it." He also suggests that "actions build character" and the workplace should be viewed as an all-encompassing learning experience.

Similarly, Angela Almeida, in The Atlantic, addresses the "worn narrative" that millennials are, in essence, "moochers," living off their parents. She counters this notion with statistics that instead imply that the labor market favors the young workers more so than their elder counterparts. She weaves a personal tale of both young millennials and middle-aged Baby Boomers searching for jobs in a stretched market. The different tactics and responses of both age groups makes for an interesting read.

In Email, Emojis Aren’t for Everyone

26. January 2017

The rise in digital communications, and the use of technologies such as email in both personal and professional capacities, can blur the lines of etiquette. When it comes to the do's and don'ts of e-communication—especially when emailing with colleagues or clients—consider turning to an article shared by Jacquelyn Smith of Business Insider.

Smith's helpful email etiquette guide, titled, "17 Rules of Email Etiquette you Need to Know," was concocted with the help of Barbara Pachter, a career coach and published author on topics such as business etiquette. Many of the 17 rules Smith and Pachter lay down are generally common sense for modern email etiquette. Yet they serve as useful reminders.

For instance, most people know to use a professional email address (instead of something along the lines of labrat123@sciencerules.com). But lesser-known rules are also noted on the list, such as the belief that all emails should be replied to—even if you weren't the intended recipient. Pachter says that this often overlooked courtesy “serves as good email etiquette, especially if this person works in the same company or industry as you.”

Job Search, Networking, Toolbox, Communication Skills

An Inspiring Challenge

17. November 2016

The last few weeks seem to have been characterized by the term "change." While each new change to the status quo impacts our daily lives at varying levels, as a whole, they are causing us to look towards the future with both concern and hope.

Here, in her recent CAM Lounge, OSA Senior Member Dr. Patricia Bath takes a moment to focus on hope and provide some inspiring advice for future and current scientists. Although, those familiar with Dr. Bath's groundbreaking work, briefly summarized by Biography, will find her admonition "do better than I did" to a daunting challenge!

Women in Science, Inspiration

The Mid-Career Transition

28. October 2016

There are many reasons to begin searching for a new job or career path—and just as many everyday-life hindrances, such as recurring bill payments, that make taking the leap into a new position unnerving. Sometimes self-doubt is the biggest deterrent to change.

Luckily, Engineering and Technical Jobs, in the article You Can Completely Change Your Career - and Your Age Won't Matter, has compiled the key questions to answer in determining whether you really are ready to make a career transition. These rather straightforward questions, such as "How happy are you at the moment?", are surprisingly thought-provoking as you find yourself answering the real question: "Is job dissatisfaction the real source of this unhappiness or are there other areas that can be addressed as well?"

No matter how confident you are in your skills, experience and knowledge, the job search arena can be overwhelming, even if you have only been out of job-search mode for a few years. To help ease the shock, several online resources, like Job Seeking for the First Time in Years: How To Do It, another article from Engineering and Technical Jobs, offer simple guidance for established professionals who are re-entering the applicant pool. Tips range from updating your CV to brushing up on the latest advances in the field. Taking courses to expand your skill set and volunteering while on the job hunt are great ways to constructively use any employment gaps!

Job Search

Conquer Social Anxiety One Conference Reception at a Time

18. October 2016

With the autumn meeting and conference schedule in full swing, the air is frequently buzzing with new scientific advances and applications. The plenaries and panel talks are invigorating and inspiring. Then come the anticipated, yet dreaded, cocktail hours and receptions.

A great opportunity to engage in further discussion and to forge new connections for future collaborations, the social aspects of conferences are also a huge source of anxiety for some. Although attendance isn't mandatory, many people feel pressured to attend - and, once there, are awkward and unsure of themselves, marveling at the conversational ease displayed by the extrovert in the center of the room.

If this sounds familiar, you are not alone. While there is no instant cure for social anxiety, Forbes contributor Megan Bruneau has 5 Hacks For Overcoming Social Anxiety and Networking Like a Pro that you can begin implementing today. Don't be discouraged or skeptical of the beginning step, "Change Your Relationship to Anxiety" (as if you need help being aware of it!). The small steps advised in the article center mostly around mental preparation, like remembering that anxious thoughts are not "objective truths." We found that what's in this resource can be applied to many other aspects of life, not just large scientific conferences.        


Are Your Online Profiles Haunting You?

14. October 2016

The internet has revolutionized the job hunting process in some really great, and some not-so-great, ways. A prime example of the double-edged nature of internet-enabled capabilities is the internet name search. Many experts strongly advise active job seekers to ensure that if a prospective employer were to search their name, most search engines will return their most relevant resume information. But what happens after the job offer is accepted? How many of us remember which websites we posted our profiles and resumes on, let alone return to them to deactivate our accounts?

Those mysterious and now out-of-date profiles are not just cluttering the spam box of your email with new alerts; they may actually be impacting your online presence, or digital footprint. Long after the job search ends, your digital footprint is continuing to speak for you to a wide variety of audiences including current and prospective colleagues, fellow conference attendees, potential clients, and even funding sources.

In her article, Ten Ways Your LinkedIn Profile is Hurting Your Credibility, Forbes contributor Liz Ryan lists simple things to do that will keep the popular networking website from becoming problematic. The first, and biggest, piece of advice: Keep your profile current! Review and update your professional activities every few months while they are still fresh in your mind. An updated profile can also make networking at events much easier. As Liz points out in the article, "it only takes a moment to grab someone’s business card when you meet them, and to ask them, 'May I send you a LinkedIn connection request?'"   

Your Digital Footprint

An Extra Dose of Inspiration For This Monday

3. October 2016

As The Optical Society celebrates the past 100 years, we cannot help but look towards the next 100—a period that some scientists are referring to as the Century of the Photon. At several events this year, OSA is hosting a series of Centennial Authentic Moment (CAM) Lounges to give members a chance to share what they find exciting about optics and photonics, as well as their visions for the field’s future.

Over the next few months, we’d like to share some of the videos that we have found to be particularly inspiring. After all, on some Mondays it takes more than that first cup of coffee to get you through those unread emails!

Here, in a discussion of his current work, OSA Fellow Kishan Dholakia, University of St. Andrews, U.K., poses some very interesting and inspiring questions, such as: does quantum friction exist? Visit the OSA Stories page for the entire collection, and keep a lookout at upcoming events to record your own CAM Lounge video!


Bright Futures Q&A: Debbie Berebichez

28. September 2016

OPN recently had the opportunity to talk with Debbie Berebichez about an increasingly hot topic for physics graduates: careers in data science.

Berebichez received her Ph.D. in theoretical physics from Stanford University, Calif., USA, but opted to not pursue an academic career. Instead, she sought out roles unexplored by most scientists. Berebichez first combined her love for communicating science in her online video series, “Science Babe: The Science of Everyday Life.” In these videos, she explained basic science, like the inner workings of a microwave oven. The videos attracted the attention of Oprah Winfrey, and in 2007, Berebichez was invited to be the keynote speaker at a conference on women’s leadership organized by Winfrey and her team. From there, Berebichez went on to host scientific television shows—even while working a day job on Wall Street.

Berebichez is now the chief data scientist at Metis, a data science-training company. Here, she tells us of her unique path to this burgeoning field and how other physicists can make the same transition.

OPN: Many would assume physics and data science are quite different. Tell us about your journey from one field to the other.

I decided to leave academia in 2009, and that’s when I became aware of “quants”—physicists and mathematicians on Wall Street.

I met physicists who were happy applying their quantitative skills on Wall Street, so I decided to try it. I first worked for a year for a quantitative hedge fund. I enjoyed it. What I was doing there—even though it didn’t have the name of data science—was essentially data science … I found the math fascinating and challenging.

OPN: You then spent six years on Wall Street. Can you elaborate on how you were eventually introduced to data science as a field?

I heard of data science late in the game; I guess I had never heard the term. My friend Hilary Mason, a renowned data scientist, invited me to speak at a conference called DataGotham. I talked about my work in finance. People approached me at the end claiming that what I was talking about was data science. That was funny, because for me it’s always been quantitative science—I didn’t really know what data science was. I started to find out more and more.

I left Wall Street, and it was quite easy, actually, to find a job in data science. They really crave people with physics backgrounds. Plus, if you’ve had some experience with Wall Street, they really like that combination.

OPN: Metis, your current company data offers science boot camps—intensive courses lasting a short time. These seem to be quite popular. Can you tell us more about them?

Our boot camp is a 12-week immersive program held in either New York, N.Y., USA or San Francisco, Calif., USA. Our instructors are experienced senior data scientists. They teach students about Python (a programming language), statistics and algorithms. Students also learn about machine-learning, deep-learning and big-data tools such as Hadoop and Spark.

The boot camp is structured around project-based learning. Students complete five projects throughout the 12 weeks. At the end of the program, students present their final project in front of an audience of companies that will hopefully hire them.

OPN: How does one become a student at a (Metis) boot camp?

We have an admissions process that’s just like a university’s. Applicants get two interviews with instructors where they (the applicants) answer technical questions. We admit about 35 to 40 percent of our applicants.

If we feel that an applicant is going to struggle in a boot camp, we’ll reject them. We let them know where their weaknesses are so that they can apply in the future. We’ve had many people who’ve come back to us that way. We also give about 60 hours of pre-work, to get participants up to speed with what they’ll be learning, and to homogenize backgrounds.

OPN: Boot camp sounds intense. How do students adjust?

We deal with the “imposter complex” quite a bit the first few weeks. We tell students that we want the water level to be at their neck, so they’re not completely drowning, but it’s not the shallow end where people can comfortably walk. This way everybody feels challenged.

Boot camp feels like an incomplete process, because it doesn’t feel like you master everything. But that’s part of what data science is, since it’s such a complex field. You’re never going to know everything; you’re never going to master all the algorithms. As long as students are comfortable looking things up and thinking on their own, then we’ve done our job. But it is a challenge.

OPN: If somebody’s coming into data science from a physics background, what are the holes that somebody in that position might need to fill?

You will realize—if you try to move into data science—that physics is an immense gift. Physics is the basis for so many things; it helps people acquire the essential skills of data science—how to solve problems and how to communicate the solution of those problems to stakeholders—no matter what field you’re in. I haven’t seen any other type of preparation that is better at taking the plunge and solving problems than a physics background.

OPN: What is the best way to prepare for a career in data science?

Boot camp is certainly a great option; we’ve had many physics students and other quantitative backgrounds come through the boot camp run. The biggest challenge is the softer skills like communication … it’s almost like they get rusty with that after spending many years in the lab or in academia.

Besides the boot camp, there are plenty of resources out there. There are many Coursera courses online—even universities are getting on the wagon and offering data science courses (though my own view is that those tend to be very slow compared to the boot camps).

OPN: Is there a place in data science for those who are further along in their careers? Say a mid-career professional in optics wanted to make a career change—would data science be accommodating to someone in that situation?

I would venture to say that Metis’ oldest student was close to 60. The incredible thing is that the salaries that you start with in data science are a lot higher than the ones that people have in academia or research.

It’s very interesting to see people who are mid-management or at the executive level in their careers come and be humbled by this boot camp—it’s challenging ... They definitely go through this sort of shaky month or two. They question if they’ll ever come out successful on the other side.

OPN: But you’ve had mid-level or executive level professionals find success in boot camp, yes?

Yes. Many of the students, once they get the hang of it and learn it’s okay to get your hands dirty, and not be the best for three months—and really try to learn as much as you can—are the students that are older and get amazing positions. They renew themselves and have this new lease on their professional life, because they never thought at 45, 50 or 55 they would be able to get jobs.

OPN: What are the big growth areas in data science going to be?

There are many cutting-edge techniques. For example, natural-language processing is applied to many different fields­­—from marketing to artificial intelligence (AI) and spatial analysis. Medical device companies use data science. Anything that has to do with what’s called the “Internet of Things,” like putting sensors everywhere to optimize climate control in a factory, or to optimize the driving of a driverless car, also use data science.

All the independent AI stuff requires an immense amount of data science power … it’s a huge, booming industry hiring data scientists. Therefore, anything that’s related to products that collect data—and needs to be analyzed for insight—requires data science.

OPN: Data science has also made its way into traditionally less technical fields. Tell us about some of the advances there.

There’s quantitative marketing. Many online ad agencies struggle to know the impact of their ad campaign; they want to have smart advertising. They’re not happy with simply putting an ad on TV and not knowing. Obviously, that industry has evolved a tremendous amount.

Even trading companies like DataMiner (who trade based on social network data) do a sensitivity analysis to see, for example, what items are becoming popular on Twitter before Black Friday. They apply data analysis techniques to know how to influence the market and sell different products.

OPN: What sort of companies are hiring data scientists?

All the big online companies, like Facebook and Google, are constantly looking for data scientists because they want to be able to recommend products to people, and they want the recommendations to be refined and targeted. They use machine-learning methods, like collaborative filtering and classification algorithms to find people like you. They’re recommending what they have bought or find your historical pattern and recommend products based on that.

OPN: What sort of innovative work is coming out of data science?

I find deep learning to be fascinating. It’s part of this cutting-edge area that combines machine learning with neural networks. Neural networks were something that physicists tried to use many years ago, but didn’t have the computing power. Now it’s a way of finding the answer to deep questions, including science questions. I believe IBM’s Watson may be using this with their successful bio part. That’s an area that’s more cutting-edge, and companies that are doing virtual reality and AI are getting into it.

OPN: You’re speaking at the Strata conference on how data can be misleading through the poor use of statistics. Can you tell us more about this?

I think that’s a question that is deep in my heart. Coming from physics, I want to teach data science not as a memorization book, but as a critical thinking exercise. But that experience of people working very closely with data—and being incredibly savvy at manipulating data—and yet not knowing what they’re doing, that is everywhere in data science. It’s really alerted me to the necessity of having people think through their datasets and what they’re doing, before they become savvy at data mining.

Dr. Debbie Berebichez is the chief data scientist at Metis. She is also a physicist, TV host and STEM advocate. She graduated from Stanford University, Calif., USA, with a Ph.D. in Physics, and received undergraduate degrees from Brandeis University, Mass., USA. Berebichez is a co-host on Discovery Channel’s Outrageous Acts of Science TV show.

Photo by Bruce F. Press Photography

Career Path, Nontraditional Science Careers, Profiles