Leadership and management in bioanalysis (4) – The unique challenges of managing scientists

If you are a scientist, it is no surprise that at some point in your career you will end up managing other scientists. I find myself very fortunate to have the opportunity to lead a lot of brilliant people in my career, as a result of my involvement in science. However, I think scientists are a unique breed when it comes to management so I am going to share my thoughts and experiences in this edition of my management column.

I have been leading teams of scientists for most of the last 15 years of my career, but recently my role became focused exclusively on scientific leadership. I am now leading a team of 15 scientists, nine of them holding PhDs. In the pharmaceutical industry we become used to working with very intelligent, highly educated people. It takes a different approach to lead them successfully. Hopefully, a few of my thoughts will help you.

I have found in writing these columns that I have more to say than space to write. Consequently, for this column I will focus on two aspects of leading scientists that I’ve spent a lot of time trying to work out. The first is how to measure the productivity of a scientist in a research environment and the second is career development for scientific staff.

How do you measure productivity in a science team? This is a problem that I do not have a solution to, but I do have a lot of experience in trying to find one. Method development productivity is generally where we focus our attention with the top scientists on our team. We set metrics around how many methods to develop in a year, how many weeks it should take to develop a method, etc. The obvious argument to this ’rigid‘ approach is that not all compounds are the same degree of complexity. I’ve seen projects take a week and some take months. My philosophy on this aspect is twofold. First of all, I implement what I call the Heisenburg principal of bioanalysis. In case you don’t recall it, Heisenburg said that on a quantum level it is impossible to know both the position and momentum of a quantum-sized object. This is because the measurement itself causes a disruption in the location or speed of the object. In terms of bioanalysis, I have observed that by measuring the phenomenon of method development execution, we see a positive change in the output and productivity; scientists output goes up when they are being measured. Secondly, I believe that 80% of projects will average out to a consistent time of execution that can be reliably measured. You can easily throw out the outliers and assess the whole package of work for a year or whatever period of time you are assessing.

Another consideration in measuring output is the question of whether a more senior scientist should be developing more methods faster or whether they should be able to develop more complex methods in the same period of time that someone else develops an easier method. I don’t think that is the case. In my experience, method development teams tend to be very collaborative and this is where the expectations for a senior scientist should be increased. The mentor role of a senior scientist is critical and must be encouraged. The time to execute projects should be similar, but the more experienced scientists should be providing guidance and training, and this should be part of their expectations. To even things out, junior scientists can pick up some simpler tasks so that there is a good balance of method development projects and extra tasks across the generally broad skill-sets in your method development teams.

The final area that I like to see for output is in scientific development. This is the easiest area to measure. I expect all of the scientists in my team to participate in some form of scientific development from continuous learning, conducting training, preparing posters and writing papers. These are activities where the output is easily measured. The team is generally enthusiastic but the challenge becomes how to prioritize and fit all of that work in. Prioritization of laboratory work is a discussion for another time.

Career development in your scientific team is another significant challenge. Scientists tend to follow a couple of paths in their career and I don’t think either is optimal for the scientific performance of your organization. One path that scientists often take in their career is to move to management. This is a natural shift for a good number of scientists. That is of course important, as we need more good scientists/managers in leadership positions. One of the primary goals of this column is to reach that group. However, there are also a number of scientists who make the move to management for the wrong reasons; because they want more money, a better career path or are pushed by their company because they seem to be natural leaders. The last one is often the most damaging as non-management leaders in the laboratory are so critical to success. Too often I’ve seen scientists become managers and immediately lose a little bit of respect from their non-management peers.

The second common occurrence in a scientist’s career is to ‘top out‘ too early. In bioanalysis it seems that a scientist in a purely method development role can top out after around 10–12 years of experience. They might have moved from Associate Scientist to Senior Scientist in that time period. The challenge for the manager then becomes identifying the next challenge for the scientist. The best scientific organizations will find ways to stimulate and continue to grow their scientists. There are a number of ways to do that. Mentoring opportunities are one common direction. Often I’ve observed that my scientists very much like to teach and lead, they simply don’t want to be bogged down with what they see as the headaches of management, such as more paperwork, meetings and personnel issues. Another approach is to identify new areas for them to grow into. This can be new scientific areas or extra non-management responsibilities. Bioanalysis provides many opportunities in both of these areas. Recently, I’ve seen scientists in my small-molecule team take on challenges in large-molecule quantitation by LC–MS/MS. Other scientists have moved from small-molecule LC–MS/MS work to the immunoassay or flow-cytometry teams. In the case of different responsibilities, I’ve recently had staff take on responsibility for writing standard operating procedures, playing a role in laboratory IT or managing instrument maintenance or scheduling. In my case, my manager many years ago recommended and supported my pursuit of a Doctor of Philosophy degree. It was a difficult path that she started me on, but I am forever grateful.

I know I don’t have all the answers but I hope that a few tips on two of my favorite and most challenging topics will help you with your end of year reviews, goal planning and general interactions with your scientific staff.