Recruiting scientists for industrial jobs

I’m passionate about recruiting. There are few decisions that a manager makes that are more important than hiring decisions. Your philosophy and standards for recruiting are a key driver for the improvement of the capabilities and long-term growth of your group. As my boss recently told me, “The talent of your staff is one of the caps on your performance.”  I started working with our internal recruiting team in 2007 and have never stopped since. The main reason is that I see it as a key responsibility to the organization. The great fringe benefit, though, is getting to know a large group of incredibly talented students and an only slightly smaller group of incredibly talented professors.

Back in March, I had the privilege of being on an impromptu panel on careers in industry for physicists at the APS meeting in Baltimore. What struck me was that my colleagues from Dow, DuPont, and other companies had very similar views about recruiting to what I had learned. The students in that session asked us great questions about how to get hired for an industrial position. Inspired by this, I wrote a short piece on the plane ride home, which the great folks at Physics World published in the September 2013 issue.

I’m hoping that in the near future, my colleagues at Science on Google+ and I will be hosting a Hangout on Air for science and engineering students at all levels to ask a panel of industrial scientists questions about their careers. Keep an eye out for that in the near future.

This is your brain on management

Here’s a great piece from the Neuroskeptic blog. They’re covering this new paper from PLoS ONE about fMRI imaging of manager’s brains.

The upshot of the paper is that managers tend to use less of their brain to make decisions, relying on established heuristics rather than fully engaging their cerebral cortex. This leads to fast, efficient decision making.

The implication I take away from this is that you get managerial failure in highly unusual (Black Swan, perhaps) situations that cannot be handled with existing heuristics and that managers are probably not inclined on the first blush to think outside the box.

Being in a managerial position myself, but one where I still am actively involved in the lab and in product development, I find that I appreciate the time I have where I’m not being expected to make rapid, effective decisions. It could be the case that letting your managers exercise these other areas of their cerebral cortex will produce better managers, ones who are able to make the rapid decisions that this paper indicates they can make, but who still have the facility to think more fully about the decision.

Nature Magazine’s job satisfaction survey

Nature has just published the results of their 2012 Salary and Satisfaction survey. It’s interesting reading, especially in light of the global economy. The primary takeaway is that in countries that have seen the most disruption, scientists are generally more worried about the stability of their funding sources and their jobs. This is unsurprising and mirrors the economic uncertainty felt in other professions. What is more surprising is that in some countries that have had relatively less economic disruption, such as China, India, and Japan, job satisfaction is lower than in countries like Spain, Italy, and the UK. A sidebar in the article speculates based on survey responses that factors such as the lack of good mentors or the lack of academic freedom contribute as much to job satisfaction for scientists as the economy.

Readers of Daniel Pink’s Drive will immediately be thinking about his trinity of motivation: autonomy, mastery, and purpose. Certainly, this particular result of the survey seems to be indicative that failure to provide these things leads to dissatisfied workers. The countries with the lowest satisfaction correlated with the countries that scored lowest on ‘degree of independence.’

 

The Perils of Highly Interconnected Systems

Technology Review has a great article about complex, interconnected systems and the risks associated with them. I suspect that this is a teaser for the authors’ new book on the subject, which ought to make for interesting reading.

Making highly interconnected systems robust is not a trivial problem, but the early pioneers of information theory developed some pretty good ways to ensure fidelity over networks. It makes sense to me that this work would be the basis of increasing robustness in modern systems. All redundancy comes with a cost, however, and it will likely be insurance companies that will lead the way in placing a value on robustness.

Nature cannot be fooled

This quote from Richard Feynman in the appendix of the final report on the Challenger disaster should be remembered by all scientists, whether in industry or academia.

Derek Lowe at the Pipeline Blog writes most eloquently on the subject: “Not even with our latest management techniques can nature be fooled, no matter how much six-sigma, 4S, and what-have-you gets deployed. Nothing else works, either. Nature does not care where you went to school, what it says on your business cards, how glossy your presentation is, or how expensive your shirt.”

Renewable Energy 101

This Friday, I’m going to be speaking at Asheville Green Drinks about renewable energy. The event starts at 6 pm and I’ll start talking at around 6:30. The blurb about my talk is up on the AGD website already, but I wanted to write a little bit about why I’m giving this presentation.

Talking to lots of people has made me realize that it is easy to be overwhelmed by the quantity of information out there about renewable energy.   Energy production and consumption is a complex topic and it is made more complex by those who have the most financial interest in the field tossing out truths and truthiness, often out of context, in order to solidify their position. And without some kind of base level of knowledge, its impossible to think critically about the news and propaganda that’s flying around in the media.

What I want to do is to give a quick overview of the state of the art in renewable energy – pros, cons, myths, and challenges. In addition, I’m going to talk about the size and scope of the “energy problem” that the world is facing and why its of utmost importance that we solve it, rather than deferring it or succumbing to it. I’m going to talk about why energy is the only true measure of wealth and how access to energy is a human rights issue. And, I’m going to end up by giving my perspective on what the ultimate solution will look like.

Its shaping up to be an exciting presentation.

Financial metrics

The capability and appropriateness of measurement systems and related metrics are not just things that scientists and engineers must care about. This seems to be obvious to just about anyone, unless you were on Wall Street before the financial crisis.

The Deloitte Center for the Edge has published a report on the decline in the return on assets of American businesses over the past 40 years. Jon Taplin, a professor at USC, posted a very insightful summary of the report, likening this decline and how it was hidden to a shell game. What was interesting to me in his post was how the blind obedience to a particular metric has been in large part to blame for our current financial insanity.

What the Deloitte report points out is that companies have been able to “juice” their return-on-equity (ROE) numbers by consistently taking on more and more debt. Meanwhile, their return-on-assets (ROA) have fallen steadily. If you are even a casual investor or small businessperson, you’ve probably heard of ROE and why it is important. You may not have heard of ROA. Let me briefly explain the difference

Return on equity is a company’s annual net income divided by total shareholder equity. Shareholder equity is essentially how much money investors have put into your company, so ROE measures how effective you are at generating a return on invested funds.  Return on assets, on the other hand, is your annual net income divided by total assets. ROA, therefore, measures your effectiveness at generating a return on everything the company owns and is in the bank.

You may already be seeing the disconnect, just based on my choice of words when I defined ROE and ROA above. Let me give you an example: Let’s say you have two companies, A & B. Each of these companies generates 1 M$ per year in net income. Each of these companies has 5 M$ in equity on the books, meaning that the investors have 5 M$ in them. In each case, the ROE of the company is 20%. Not too shabby. But there is an important difference between them. Company B also has 5 M$ of debt outstanding. Company B will thus have 5 M$ more assets on the books than Company A, and thus their ROA will be lower.

If you’re a CEO and you’re managed by your board on the basis of your ROE, you thus have a substantial incentive to leverage your company with loads of debt in order to have more resources with which to expand your business, since that debt doesn’t show up directly on your measurements. People will still invest in your company on the basis of your keen ROE (so long as they don’t look at your debt-to-equity ratio, or your actual return on assets.)

The bottom line here is that a lot of people had a warning right in front of them about what was happening with GM and other companies, but couldn’t see it because one of their chief metrics hid it from them. As with so many other things, relying on a few simple metrics is dangerous and sloppy. Simple metrics are useful, but they must be cross-checked and reviewed with a constant eye on exactly what they tell you and what they do not.