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Get yourself a better bioinformatics job

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Bioinformatics London, May 2020
An opinionated view of how biomedical informaticists shoot themselves in the foot when it comes to looking for work

Published in: Health & Medicine
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Get yourself a better bioinformatics job

  1. 1. Bioinformatics London (May 2020) Get yourself a better bioinformatics job (or career) Paul Agapow paul@agapow.net
  2. 2. Warning ❖ Some of this is to do with bioinformatics ❖ Some veers towards general job seeking advice ❖ Because, looking at your applications, you need it ❖ All of this is opinion, albeit based on experience
  3. 3. Who am I? (to be giving you advice) ❖ I’ve done everything ❖ Biochemist & immunologist ❖ Evolutionary scientist ❖ Bioinformatician ❖ Epi-informatician ❖ 'database manager' ❖ 'computer guy’ … ❖ I’ve made every mistake
  4. 4. The truth: it’s tough out there Where bioinformatics ∈ {genomics, comp. biology, biostats, health informatics …} ❖ Underpaid relative to skills ❖ Under-employed but over-worked ❖ Under-appreciated ❖ Have to do everything ❖ Precarious employment, short-term contracts ❖ No advancement, others get attention ❖ Culturally, bioinformatics has been bad at selling itself
  5. 5. But things are much better than they used to be ❖ Demand for skills ❖ More flexibility ❖ Bioinformatics as industry ❖ More fluid academia-industry movement ❖ Biomedical science is data science ❖ Especially in the current crisis You are more in demand and have more power than you think
  6. 6. Rule 0: what is is that you want? ❖ Leadership? ❖ Work with cool tech? ❖ Work on cool problems? ❖ More money? ❖ More respect? ❖ Do something with impact? ❖ A secure job that pays the bills? Any of these is cool. But be clear what you want.
  7. 7. Rule: a good job doesn’t just happen to you ❖ A lot of people will advise you to wait, bide your time, put your dues in ❖ Something will come along ❖ This is a lie ❖ Often out of self interest ❖ Good things do not come to those who wait ❖ Good jobs (in the abstract) must be pursued
  8. 8. Rule: a career is not a straight line ❖ Academia & CVs love telling uncomplicated stories of how people were triumphant, and sneer at ‘generalists’. But this isn’t how the world works. ❖ Careers are zig-zags or spirals ❖ Areas go in & out of fashion ❖ You can try new things and move on Tell a good story, but don’t be afraid to evolve your skills and career Don't look for the perfect job, look for a job that will give you closer to the perfect job
  9. 9. Rule: maybe don’t call yourself a bioinformatician ❖ A job application is a sale, so sell ❖ What words are going to look better on your application? ❖ If you can claim it, do so! ❖ Bioinformatician may not be one ❖ Staff scientist? ❖ Researcher? ❖ Computational biologist? ❖ Healthcare data scientist? ❖ Expert in ML over patient-level records? ❖ A bioinformatician in SOMETHING?
  10. 10. Rule: be ruthless in trading up ❖ You do not get ahead by staying in the same place ❖ Especially as regards salary ❖ Hirers tend to look outside and respect outsiders ❖ 18 months to 2 years at a job is fine ❖ Look at job histories of high-fliers ❖ Also means a job only has to be good for 18 months to 2 years ❖ Maintain a constant soft job search
  11. 11. Rule: streamline your job search ❖ The job search can take a lot out of you ❖ It can hit your moral ❖ Automate it as much as possible ❖ Alerts, mailing lists, recruiters ❖ Mark yourself as looking for work on LinkedIn ❖ Have a list of places you scan regularly ❖ Tend to it once a week at maximum
  12. 12. Rule: it’s a numbers game ❖ What’s the Drake equation for your job search? ❖ There’s a lot of randomness (and unfairness) in the process ❖ Mostly it’s not to do with you ❖ Many “jobs” don’t actually exist ❖ Don’t take it personally, don’t over-invest ❖ Get your numbers up ❖ Take a few chances Does the job exist? Is it earmarked for someone already? Can you get shortlisted? Can you get an interview? Can you get an offer? Will you want the offer?
  13. 13. Rule: punch above your weight ❖ If your job applications are not getting rejected, you’re not aiming high enough ❖ Jobs are lost and won by all sorts of random and ridiculous things ❖ You might get lucky
  14. 14. Rule: be visible If I had to give only one piece of advice, this is it: BE SEEN ❖ Bioinformatics work discourages this but: ❖ If people know of you, they will think of you ❖ If they recognise a name on an application, you’re halfway there ❖ Give talks ❖ Network ❖ Social media (they will google you) ❖ Don’t be silent ❖ You don’t have to be a rockstar, but you have to exist
  15. 15. Soft skills are valuable ❖ As you progress in your career, technical skills diminish in importance and soft skills rise ❖ You can get hired just on the basis of your soft skills ❖ Most people don’t have any evidence of them ❖ If you do, that will stand out ❖ Even if you’re not naturally extroverted (etc.) you have to still speak up, manage, communicate. You don’t have to be great, just okay at this.
  16. 16. Rule: your CV is terrible and you should be ashamed ❖ It's too long ❖ It's too hard to find out who you are & what you can do ❖ There's too much minutiae ❖ You've tried to bullshit me Hirers have limited time and patience. If you make them expend it unnecessarily, they will dislike you. No one is going work to understand it.
  17. 17. What should a CV look like? ❖ Ignore most hard-line detailed rules ❖ Short, easy to read ❖ Lots of bullet-points, lots of spacing, phrases not sentences ❖ Repeat keywords from advertisement ❖ Ideally 2-3 pages ❖ No hobbies, easy on software packages / platforms / details ❖ Abstract or exclude anything more than 5/10 years old ❖ Are they really interested in teachers and that poster you presented 7 years ago?
  18. 18. Make it easy to just give your CV to someone “Uh, yeah, I’ll send my CV to you, just give me a week for me to update it …” ❖ Always have a current CV ❖ Treat it as an iterative document ❖ Have a master copy ❖ Hone it for every job ❖ Direct it to the job requirements & keywords (T format cover letter) ❖ You only have to be 80% of a perfect fit
  19. 19. Rule: you can move between academia & industry ❖ It used to be very difficult (and one-way) ❖ Some academics still look down on it ❖ But it’s become very common ❖ Now industry looks down a little on academia ❖ Best to get something on your CV early?
  20. 20. “Industry” is a lot better than you’d think ❖ Much better pay ❖ Quicker turn-around of projects, that do something ❖ Better resourced ❖ Better science, cool technology ❖ Treat their people better because they want to keep you
  21. 21. Rule: be pleasant, not “nice” ❖ Personality is the biggest failure in interviews ❖ “Can I work with this person? Are they going to cause problems? Will they speak up if there’s issues?” ❖ It’s very hard to assess whether someone can do the job so often it comes down to whether they like you ❖ But don’t go over-board: ❖ Be interested but not over-eager ❖ Be positive & a problem-solver but not hyper ❖ Be assertive but not aggressive
  22. 22. Rule: be cool in interviews ❖ Being calm radiates confidence ❖ Pause before answering ❖ Ask for clarification ❖ Have stories, know your own work ❖ Have questions! ❖ What’s your point of pain? ❖ What’s bad about working here? ❖ Who are your competitors?
  23. 23. Rule: red flags ❖ If you interview in front several different people, do they agree on the job? ❖ What can you tell me about this job? ❖ Sometimes you will end up in an obvious charade of an interview. That’s okay, it’s still practice. ❖ They can’t actually tell you what you’d do (so why are they trying to hire?) ❖ Job titles like: database manager, web developer, support, engineer ❖ Interview process is chaotic
  24. 24. Rule: negotiate ❖ If you don’t negotiate, you’re being taken advantage of ❖ Never give your current salary ❖ “I'm more interested in the actual work” ❖ “I'm sure you know what the going rate is for people like me” ❖ But do your research (Glassdoor?) and don’t overbid
  25. 25. What’s hot at the moment? ❖ Patient & healthcare analytics ❖ Biomedical analytics in general ❖ Being cross-functional ❖ Machine learning (duh) ❖ Knowledge graphs ❖ Single cell technologies ❖ Real World Evidence
  26. 26. Good luck!

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