The company I joined in January sells complex biochemical reagents (oligomers) required for CRISPr editing of genes in cells for academic and pharmaceutical research experiments. Trouble is, the company’s factory can’t make these reagents well. Yields hover around fifty percent (about half of the product is trashed) with wild, unexplained swings from week to week. Our team has adopted a magic eight-ball as a prognosticating mascot to predict factory health.
These last four months, my main role within the Data Science team is compiling, charting, and displaying these yields at real time. Before I joined, the company was blind to how well or poorly production ran. Now, almost everyone can watch the election returns, metrics I call “boxscores.”
It is one step to notice the problem and another further step to fix it. Periodically my boss and the Chief Technical Officer wax fantastically with assertions like, “you need to get into the lab and start diagnosing what’s wrong.” Unfortunately, neither the lab people nor the research and development team want me prying into their systems.
Nonetheless, the company’s protocol chemist recently quit and the company has yet to hire her replacement. I’ve got a chemistry degree. I’m now on my fourth biotech start-up company. This kind of work is neither new nor scary for me. I’m made for this job.
After few awkward informal conversations with department heads, I threw my hat in the ring and applied formally last Wednesday night for the position of Head of Protocol Chemistry. This role with its team-management responsibilities would be a two-step jump in career for me and where I want to land at the synthesis (ha ha) of ten years of industrial chemistry with four years of data sciences.
Oddly, I haven’t heard back from the company. I know, it’s been only four days since I applied, but it’s troubling not to get yet either an interview slot or a polite declination. I don’t have expertise in nucleotide chemistry – I get it – but the lab needs desperately smart people to improve yields before huge projects hit (and crash on) the factory floor.
So I’m doubling down. I either get an interview slot, prepare hard for an interview, and spend perhaps a solid first year managing a small team unpacking the details of the company’s factory – or – I return back to data sciences where I have a de facto boss who is more than twenty years younger than I am, and I coast. I work from home more often, I selectively pick the projects that interest me, and with a comfortable salary I turn my life back outwards to non-work pursuits like LED art projects, travel, parties, and life.
This May month will be a should I stay or should I go month. Although I resent rejection and do want someday to run a group, both outcomes are wins for me. I’ll either be working much harder in chemistry at a higher salary, or relaxing more with computational work.