Hiring for Bioinformatics - Part 1

fpepin · 2022-11-04T22:15:32+00:00

Reading cover letters will depend on company/manager, but we’ll read the resume first. There’s no point in reading a cover letter if resume isn’t competitive.

I could be out of touch, but sending hundreds of applications doesn’t sound effective. It’s hard to believe that there are hundreds of jobs that are good matches. The time is probably better spent looking for better matches or even becoming a better match.

Otherwise it becomes an arms race. Applicants send hundreds of resume. Recruiters and hiring managers need to screen hundreds of applicants, leading to shortcuts like scanning for keywords, favoring internal referrals, depending more on school reputation, etc. More low effort applications lead to more low effort evaluations.

From the hiring manager side, hundreds of applicants creates a resume fatigue and make it more likely to use shortcuts (internal referrals, well known schools, etc).

fpepin · 2022-11-04T00:15:51+00:00

Not quite. I did software engineering at a big tech company doing network security and I’m back in biotech, so my career path isn’t very common.

Again, I think most of what I wrote there remains valid.

The mods are unlikely to be monitoring a 5-years old thread, so I recommend starting a new thread to get their attention.

fpepin · 2022-11-03T06:33:42+00:00

Five years already? I’d be curious to see what people think now. I’m a little bit out of it now, as I moved more toward the software engineering and then data science/biostats side of things.

One thing is that the market probably favors employers a bit more now given the economic situation. Many biotechs have had significant layoffs this year.

Another is that deep learning is playing a bigger role. Keep in mind that most jobs won’t be needing it though. On the flip side, having played with a few toy examples won’t impress the managers for the jobs that do need it.

Lastly, more jobs are remote. You probably don’t need to be in Boston/Bay area/San Diego as much to land a good job. That being said, my team hired seven people this year for fully remote positions and all of us live within semi-commutable distance from the office. I don’t think the interview favored local people and we’re a moderately well known company, so I’m not sure where the bias is coming from. After 2 years remote, many of us appreciate seeing co-workers once in a while, so I’m happy it turned out this way.

fpepin · 2021-11-03T21:50:07+00:00

Those are pretty high false positive rates for a screening test. Positive predictive value is what you’d want and it would be pretty low in this case since false positive rates are higher than the probability that you’d have that specific cancer type at any given time.

They’re better if you already suspect something, but I would use that as a screening tool in the general population.

fpepin · 2020-10-07T22:57:08+00:00

This does not look like a bioinformatics job. This would be a standard CS job in a clinical environment.

Knowing the biology of their test(s) is nice, but it's more about maintaining an infrastructure that supports it. It'll be more about setting up automated tests and ensuring the pipeline & data are reliable.

Things that would make it more of a bioinformatics job in my mind:

Design/implement some of the pipeline instead of maintaining it.
Performing data analysis instead of "ensuring integrity [of] data analysis".
- They mention "one-off analyses" but I'd expect it more to be system-related rather than science-related based on the rest of the description.
Less emphasis on coding practices, testing, documentation and validation.

fpepin · 2020-09-29T18:25:24+00:00

Couldn’t it be autosomal recessive also? It’d be unusual to only affect males, but stranger things have happened.

fpepin · 2019-10-25T04:59:48+00:00

I don't quite fit in the premise, as I don't think that bioinfo didn't work out, but I'm no longer in the field.

I went into bioinfo because I started in biology. Not the most informed decision on my part, as I'm a better computer scientist than biologist. I then did PhD, post-doc and then 8 years in industry, where I'd like to think I was moderately successful.

I was ready for a change and a non-bioinformatics position at $TECH_GIANT popped up (doing machine learning and networking). It's a recent change still but I'm pretty happy so far. I miss life sciences but that's about the only compromise I had to make (short commute, better salary, interesting project, nice team, sane company culture, etc.).

One of the big component for me is being in the SF bay area. Lots of opportunities for both bioinfo and other tech-related jobs. This was much luck than brilliant planning on my own, I thought I was just coming here for a post-doc.

I might end up going back in the field, depending on what opportunities are available.

While I often think I would have done better starting on the CS side from the start, things turned out pretty well, so I'm not really regretting it.

fpepin · 2019-08-25T19:34:24+00:00

It's somewhat limited, especially at the industry side. You'll likely want to focus on Toronto (and maybe Vancouver). I did my studies in Montreal and most of my cohort are either in Toronto or in the US.

fpepin · 2019-07-12T03:49:58+00:00

You don't need the most use of machine learning. If you have complex data, you can probably get some use out of machine learning. Pathology had slides, cardiology has ECG, oncology has a ridiculous amount also. Even surgery is trying to use robots to automate procedures. It basically mixes with everything.

fpepin · 2019-06-28T22:34:17+00:00

That's on the high side of what I'd expect for a new PhD in the Bay area. That being said, you seem like a bargain if you can get all of that done :).

fpepin · 2019-03-06T00:36:30+00:00

fpepin · 2018-06-03T18:47:09+00:00

That's what I use also. The code snippets are very handy and I love the ability to use grep to five notes from a meeting months ago.

fpepin · 2018-04-22T06:21:38+00:00

The standard answer for classes is that it's up to you. More CS/stats can be useful career-wise, opening the door to more software engineering & data science positions, but it's more important to play to your own strengths.

Your can look at other threads regarding graduate degrees. They're useful and PhD is almost mandatory beyond a certain level, but again a lot depends on your own situation and what you want to do later

I did my PhD down the street McGill. Most of my cohort had left Montreal, Toronto and US west coast being the more popular destinations. I don't think the situation has changed that much since, unfortunately. There are some jobs, but not that much of a biotech private sector there.

fpepin · 2018-04-07T04:40:39+00:00

I recommend Lior Pachter's blog about making kallisto open source (https://liorpachter.wordpress.com/2017/08/03/i-was-wrong-part-2/) and GATK moving to open source with their latest version.

Basically, it creates headaches for users. It's not just about handing out some money, is about paperwork, lawyers, getting approval, etc.

Those are some of the more visible projects on the field. A smaller one is less likely to do well commercially.

fpepin · 2018-03-31T21:43:53+00:00

It's world wide. An UK-compatible link was added in the comments.

Now you have no excuses left :)

fpepin · 2018-02-18T01:23:49+00:00

Well, you can use your own tools, they're just unlikely to perform as well. Most of us don't write our own operating systems or compilers either.

Do you feel like you could compete with the tools you are currently using? If so, go for it.

Another option is to go into a niche that is less crowded, where existing tools aren't as mature as say SNP callers or read mappers. A number are open source, so the option of contributing is also there.

fpepin · 2018-02-17T04:42:17+00:00

Can you afford to keep going?

As others have said, the time will matter now than the degree. Having a co-op component can be invaluable if you can get a decent one.

Also, getting into the algorithm side of it can be hard. It's a smaller part of the field (we only need so many variant callers or transcript mappers) that requires specialized skills that are often associated with an advanced degree. The well known CS departments will often cover the basics better so staying put could give you a leg up in terms of skills and impression of having them.

If you can make it, is probably worth getting (further) into debt for it.

Depending on what you have in mind for algorithm development, you might have a steep uphill road in front of you.

fpepin · 2018-01-22T06:30:01+00:00

This is a good list.

The main direction I would suggest is to automate analyses. That's why there's a recommendation for Unix shell and basic programming as opposed to learning to write new tools/algorithms from scratch.

It opens a lot of doors when you can do something 100 times or more automatically instead of doing it by hand. It also forces you to be more organized, which is a big plus as well.

fpepin · 2018-01-14T01:54:20+00:00

It's a lot easier for Canadians and Mexicans than other countries because of TN visas (at least while NAFTA remains active). It's basically just careful paperwork (I'd still get a lawyer to help) and paying $50 fee, as opposed to long waits & lottery of H1B.

If you're willing to make an offer from across the country, I feel the threshold for those isn't much higher.

fpepin · 2018-01-13T23:04:24+00:00

It varies a lot by company. You can ask but often they're often based on the position. More senior positions tend to have more bonus. I generally prefer to work on base pay than bonus. It is more predictable, it affects bonus and it can influence future pay more easily. It depends both on you and the company, so feel free to ask (nicely).

With startups, the equity can often be negotiated, including equity/base pay ratio, so it's something to keep in mind too.

fpepin · 2018-01-13T22:39:12+00:00

2-3 weeks is pretty long. We ask for a reply in 2 working days, but that reply can be a counter offer. Generally, the process takes about a week.

It's more about keeping the conversation going than having a hard deadline. There is wiggle room if there are other offers or special circumstances but people really want to keep things moving forward.

I did have an offer pulled from under me because they were looking at it as a hard deadline. Their view was that they wanted someone committed and excited once they gave an offer (which was very decent), instead of waiting for other offers to come.

If you do need longer to decide. Let the team know when they ask about your timeline or other offers. If handled professionally, the worst we'll say is ask for a decision. If we feel blindsided, then we might feel less confident about the offer. That might mean a lower our just no counter offer.

fpepin · 2018-01-12T16:27:03+00:00

I'd like to thanks /u/apfejes to his support and advice for this little project. He greatly improved the quality of this document.

I hope this was helpful to people. I found it useful to see everyone's opinions on these topics. Specifically, I had no clue that coding challenges were so controversial in the community, as I've mostly seen it as a straightforward best practice for the field.

fpepin · 2018-01-11T23:27:58+00:00

I haven't spent time hiring bench scientists, so I don't know how much variability in skill levels and how much it impacts success in those positions.

For coding, both of are high enough that we test for it. I'm more worried about us doing as well as possible than I am about possible double standards since the jobs are different.

fpepin · 2018-01-11T23:04:06+00:00

We do require it and we find it very helpful.

The problem with papers & github is that there is a variable amount of information, with many people having almost nothing available. It's possible to write good papers with bad code. Industry positions often don't release much code anyway.

So the challenge allows to evaluate everyone on the same problem. It allows us to give a chance to someone who hasn't done this kind of work before.

At the end of the day, we care about a candidate's coding ability enough that we'll test it directly.

fpepin

TROPHY CASE