When I decided to start working on my blog more, I also decided quite consciously to maintain a healthy ratio between personal and professional posts. Mostly because I find blogs that are ostensibly about a person, but that only talk about their professional work, to be boring, as that’s only half the person really. That said, the professional identity is important, it puts bread on the table and maybe one day will pay for kids’s colleges, and so it needs its care and attention as well, especially since macro business cycles can be unpredictable. We are in the midst of quite a long boom cycle since the Great Recession of ’07-’09, and of course “winter is coming”, but it’s anybody’s guess as to when.
Furthermore, when I think about my professional career, I am optimizing for a 40-year run (ages 22-60ish). I doubt I will be able to retire before 60, and of course, it’s anybody’s guess as to how easy employment will be had or kept after the age of 50, age discrimination being the increasing reality that it is, so some of that planning does require a branch for self-employment out of necessity.
Furthermore still, the idea of career planning has always been a paradox. For some people, especially who spend most of their career at one employer, it can be quite straightforward, and I’ve found talking to those people unhelpful, as they don’t have the perspective of moving around, both geographically and between roles. Some people get lucky and don’t really realize the statistical uniqueness of that outcome. The paradox is between the job you have, and the job you want (the attainable kind, not the mythical). The dissonance between those two is quite a spectrum. And furthermore, a job is not a monolithic thing; you are solving for many variables at once (role, manager/IC, technologies used, team, growth opportunity, stress level, working hours, commute, location, etc). The personal weights assigned to those variables change over time.
If I’m aiming for a 40 year career, then I’m almost at the halfway point temporally, and when my “earning power” will max out is a deep topic for another time. If you think your salary is always going up and to the right, think again folks.
Due to all these realities, I’ve cobbled together what you could call a layered approach to career management. But before I even get into those details, I like to start with a holistic view of things. And so I present the following diagram, which neatly summarizes salient aspects of my career to date:
I’m a spatial thinker, so I find this kind of visualization quite helpful. In fact, it’s more helpful to me than it is to you, because while the visualization itself is sparse, I can use the data that is there as associative cues to other considerations in my brain that I haven’t explicitly represented.
Furthermore, the title of this post says “visualized simply” and I mean that. Data visualization has really become a thing in the last 10 years or so, and I’ve seen some really over-the-top viz. Now, the line between “data density” and “why are you showing me that?” is perhaps subjective, but I prefer to start simple, and focus on the broad strokes view of what I’ve done.
I would say, when I see that visualization, I see a lot of fragmentation along every possible dimension. In the past, that was fine and in part intentional. I wanted to be well-rounded and the only way to do that was to try different kinds of roles. But, I think that chapter of my story has run its course.
Instead, my goal is to really focus my attention on the areas that are most likely to realistically play a part in my career for the next 20+ years. This is a subtle point; it has to be something which is a stable or growing area (does the world need anymore Windows COM experts? Probably not), multiplied by what I’m intrinsically and intuitively interested in.
When I started working on mobile/iOS, I thought maybe that’s the right track to double down on. But I decided against that, in 2017, and I will blog about my reasoning there another time.
The area I do plan to focus on is applied machine learning. By applied I mean non-research-focused. I don’t have a PhD, so it is highly unlikely that I will find myself breaking ground at the theoretical level. Hence, a focus on applied works.
In fact I have a loose conjecture that applied machine learning is just going to be another aspect of business logic for apps. This is something I also need to tackle in another article. The amount of specialized knowledge to wield it continues to drop rapidly. You generally don’t need to bust out a derivative or even hand-code an algorithm yourself, insofar as you can get started with many off-the-shelf packages and approaches. Of course, the cutting edge will always require bespoke operations, but the number of teams that will need to do that kind of work will drop over time, as approaches standardize, become “good enough”, and are composable.