Thanks, that is good advice.
Your first point is definitely true. There are a lot of smaller nonprofits that could use 0.2 data scientists or 0.4 software engineers, but can't hire them in fractional quantities without all of the additional hassles associated with contractors.
I have a project and a (short) list of organizations I would like to pitch. Originally the list was 'a couple of organizations I have worked with before, maybe 25% one of them will say yes or refer me somewhere useful', but I like your advice to be more proactive -- cold-emailing people is intimidating but not actually that costly or intrusive.
(At least that is what I think "Spend a weekend putting together a solution to these problems, and send them to a couple of people at the company with an invitation to talk more" is suggesting.)
I have some stupid questions about this:
My instinct is to contact someone with the minimum seniority to implement my project -- but that still means someone with hiring authority -- job titles like Program Manager, Assistant Director, or Director. Does that sound right to you?
I'm also inclined to to prefer using an individual public email address if it exists. Usually it doesn't. My guess would be that unsolicited LinkedIn messages will go to spam, but maybe I should send them anyway? Along those lines Twitter is semiprofessional these days but probably kind of sketchy and I'm not on it. Should I just prioritize people with public emails? The only other thing I can think of is organizations with 'slush' emails for general jobseeker inquiries, etc. Am I thinking about this wrong?
Thanks for your help!
Hi everyone! Like many others, I'm interested in exploring whether I'd be a good fit for an EA-connected org and would appreciate any help towards an answer.
I'm 35, finished a top physics PhD program in 2014 and have been freelancing as a data scientist since then. Most of my clients have been in finance and health, but about 10% of my work has been in international development. I've largely been a generalist, emphasizing fast and accurate-enough solutions to diverse problems.
(1) Are there any career paths outside of the 'researcher' (research analyst, postdoc, etc.) track I should keep an eye out for? For example, are quantitative problem-solving skills enough of an asset in monitoring-and-evaluation roles to offset a lack of specific experience? (I applied for a few and didn't hear back, so probably not, but it might have been bad luck.)
(2) If anyone wants a sounding board about data issues at their current org I always enjoy talking to people about their problems. I'm especially interested in opportunities to improve the ecosystem around activity/individual-level data -- this is less about being fancy, and more about frequently discovering that my aggregates are wrong because of upstream issues -- but really, no problem is too weird.
Thanks in advance!