Here is a list of words I learned in my first 3 months after joining a trendy health tech company in Manhattan.
After my past jobs in academics, non-profit, industry, and consulting, I was unprepared for the culture of communication at a tech start-up company. My new workplace was filled with mission-driven millennials, t-shirt wearing software engineers, and productive product people. I am terrible at texting, don’t use emojis, and hate google doc comments. How would my new co-workers react to my archaic, hand-written documentation?
This glossary is my gift to you, readers. Before your job interview or first day working at the tech company of your dreams, familiarize yourself with this list. Practice speaking the terms naturally in conversation with a friend (preferably age <25 years) to gain confidence.
A small sample of my colleagues at other health tech companies have confirmed this jargon is not specific to my workplace but is common across all. Be prepared.
+1 – agreement
+100 – extreme agreement
+1000 – the most extreme agreement
backlog – list of thing you need to do but don’t have time to do right now; used in formal places such as “backlog grooming” in the scrum process or agenda item backlog.
bespoke – custom, artisan product for a customer; an old word made trendy again
‘big data’ – a verbal cue that someone doesn’t know what they are taking about. For example, “He got up on stage and said ‘I use big data all the time’ and then we all thought ‘Gross! This guy is an idiot.'”
blocker – you can’t do the think you need to do because something is in the way. “That’s a blocker for me” or “Blythe is blocking me from finishing this”
code review – peer-review process where colleagues check every line of code for accuracy, consistency with a pre-specified analysis plan, and alignment with a style guide before anyone sees the result
DNB – do not book, time is being held on the calendar
high touch – to email and visit someone often so they feel connected
flavor – type of product or pathway, part of Pooja’s food-themed descriptors
fwiw – for what it’s worth
giphy – animated clip used in docs, slides, and messaging to communicate. never speak this word out loud
git – version control – never to be confused with knowledge of the GitHub website
g-suite – parallel to Microsoft Office allowing real-time collaboration; great in several way except for huge deficiency in lack reference manager or ability to integrate with reference manager for academic publication citations and unsolicited commenting
lgtm – looks good to me; used by reviewers of a document
OKRs – goals
on menu – choosing one of many options of services that are available to you; off-menu is also used
pressure test – checking lots of features or edge cases of something to make sure it is accurate. “We need to pressure test this dataset”
scrum – a style and process for project management. no one says this word out loud, unless they are explaining it as a category for related project management jargon
slack – internal messaging system. understand context listening to How I Built This. “Slack me”
sticker culture – the theme of decals covering one’s laptop, often reflecting one’s personality, hobbies, favorite software packages, company, or hometown
stickiness – the degree an external client feels like they are engaged and need us
sprint – one or two week cycles of team effort to complete a set of project tasks, part of scrum. “Let’s tackle that in the next sprint”
stand-up – short check-in meeting with group. “I’ll remote in for our standup today”
sync – meet to discuss, usually in-person. “I’ll sync with Sally about this tomorrow.” [personal note: I have flashbacks to connecting my Blackberry and computer with a cord every time I hear this]
tl;dr – a brief summary, for people who will think “too long; didn’t read”. Commonly said, “What’s the tldr?”
zoom – virtual meeting space with video and screen sharing. “I’ll zoom you later.” If you mention Skype people will know you are too old to deal with.
I skimmed the Labor Force Statistics from the Current Population Survey from the Bureau of Labor Statistics for data on the distribution of age at a normal places in the United States. The evidence supported my hypothesis. Results are presented in Figure 1.
Suggested additions to this glossary are welcome as comments.