This essay kicks off a series called Figures of Merit, in which we  apply analytical methods in non-scientific environments. We’ll start by exploring the relationship between time and money for early-career scientists. The question: how much is our time worth? Not only is this this a difficult question to answer  – but for academic scientists beginning a career in industry, it is a question rarely asked. If you want to skip the analytical part, here is the message: I can’t tell you the value of your time, but neither can you, unless you know how to measure it.

For the data set in this analytical exploration, I’ll use a few key metrics from my own career. A career, after all, is an experiment – and like any experiment, the outcome largeley depends on our ability to observe something meaningful.  Of course this is an experiment with N of one, and not representative of all scientists. But if we consider ourselves to be in the business of reducing uncertainty (I do), and if we think career planning represents a lot of uncertainty (it does), then the best tool we have is to reduce that uncertainty is a plan. Even with a limited data set, we have something interesting to compare: the difference between Plan and Reality.   

The timeline below shows my plan as a grad student (the top line in blue): spend a couple years as a postdoc, then get a Real Job, beginning an unbroken trajectory towards Success. Success, in this case, was loosely defined as paying off loans, buying a house, and achieving some financial security. Reality (bottom line, in black) turned out to be a different story: the postdoc was longer than expected, jobs were hard to find, and keeping a job was even harder.

The first thing you might notice is the quantity of black circles: each one represents a new job. Less obvious, but even more meaningful, are the blank spaces: those represent time without work. Needless to say, this was not part of The Plan, and had many consequences not easily shown in this chart.

Much of what matters can be hidden in charts. While the visual above does highlight the number of job transitions, it does not show the impact, or cost. Here are where Figures of Merit come in. Measuring progress in academia was easy. Papers published, impact factor, grants awarded – these are the currency of research scientists and easily recogized by everyone in the field. However, this currency carries little weight outside the academic world.

For the goal I was trying to achieve (financial security), a more universal metric would be something related to money. I considered plotting student loan debt as a function of time,  but decided against it because a) it would reveal my embarassingly naive financial planning and b) it is convoluted by other decisions (having kids, my wife’s post-graduate education, buying a home, etc), and c) there is nothing interesting about a flat line.  

In my case, a more useful figure of merit is retirement savings. This is not on the list of priorities for most early career scientists, but it will catch up with all of us sooner or later. Consider:

• Paying for retirement is a necessity for everyone, regardless of career path.
• For those of us who spend extra years in school, we have less time in which to earn and save before retirment.
• Many of us live in high-rent areas, leaving little room in the budget for long-term saving.

The graph below shows my cumulative retirement savings since my PhD, in units of months (I estimated the monthly cost of retirement, assuming my overhead is roughly the same as today). Moving from left to right, there are three periods of my career reflected with three different slopes: the first is the 3+ years as a postdoc, the second is my 4 years working in startups, and the last (with the steepest slope) shows my two years to date in a large company.

There a few lessons I take from the graph:

1. My retirement savings as a postdoc was significant, even though the salary was less than half what I made later in industry. This is mainly because the university took automatic deductions to my pre-tax income.
2. I saved roughly the same amount in the two-year “corporate” period of my career compared to the first 6 years combined.
3. My retirement savings were essentially flat during the “startup” period, even though my salary was considerably higher than a postdoc. Four job transitions meant four periods of gaps in salary, gaps in health insurance, gaps in free snacks, etc.

The savings vs time graph is one way to illustrate opportunity costs when considering different career paths. These opportunity costs are not obvious when job hunting. After life as a grad student, almost any job can seem lucrative – but it depends on the metric. Thinking about long-term savings as a metric may make us consider job stability just as much as salary. On the other hand, employers at startups may offer not just immediate income, but (potentially) imminent wealth & success.  This makes the evaluation of stability vs income more difficult, because the income is partly theoretical.

When I talk to early-career scientists about the decisions they face, I ask them to identify their figures of merit. If they are concerned with long-term financial stability, I bring up the lessons shown here. But this is just one example for one individual. More importantly, each person can identify what is important in their life. Money, fame, vacation time, respect among peers – these are all legitimate ways to measure a career. The real question is: can we have the foresight to plan our careers, making choices to optimize for the things things we care about?

OK – we’re scientists. We know it’s easy to apply analytical thinking to a bunch of things that have already happened. The trick is to use that thinking learn from observation, build models, and plan ahead. We do it all the time with our experiments. Now how would it look if we apply the same thinking to our careers? I look forward to hearing from readers – what are your figures of merit?