Posted on August 27, 2017
It’s been some time since I wrote, but I’m finally diving into new and interesting problems that matter to me, and realizing I need to bring my analytical skills up to par quickly. I have to not only do projects using R and learn it more deeply than I ever did before, but I also need to learn to think from first principles again. The only way to learn this is by doing, i.e. by working on problems I care about. The stakes are real, my time is limited, but my ability to focus, imagine, and iterate are boundless if I’m willing to harness them.
Every day at work, I’ve got a chance to learn Project Management in a rapidly-changing startup environment, unlike any other I’ve been in. Let’s seize that, making the most of my work hours to create the project structure and tracking we need to succeed. It’s chaotic and requires re-working of processes and tools, but these iterations are also a chance to improve in meaningful ways. I can get better at anticipating challenges our projects will encounter, the needs of Growth, Product, Data Science, and Engineering, at quickly understanding our new Product vision and building the structure to support it, and ramp up on tools like JIRA and ways to share progress with an entire organization.
Further, we have an increasingly interesting data set at work and I have time and motivation of my own to develop my analytical skills. I’m convinced that rare and valuable skills are the surest currency, that focus is the first such skill, that deep work is the best means to hone such skills, and that I’ve found and will find most meaning in doing such work. No more distractions, no more casting about for optionality in my work or personal life – let’s keep life simple, and do what it takes.
January 2018 Notes: Project management came to a quite end due to internal politics at Premise, but after exploring multiple options, I found a Data Analyst role in our Engineering Insights team that met my needs. The role has gotten me much more exposure to Data Engineering work as well (e.g. writing Airflow ETL pipelines), with an emphasis on exactly the engineering workflow, coding best practices, and programming approach I lack, rather than Data Science’s usual emphasis on statistical methods that I’ve already seen. It’s been great training, to bridge the gaps and take my career to even greater heights.