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Comment by danieltillett | Hacker News Reader

As someone who has gone the other way I would say no. Getting to the forefront in biotech is a 10 year full time journey. I have met a few developers who have tried to learn, but they have all struggled.

Having said this don’t worry, if you think the job prospects are bad for old developers they are far worse for old scientists.

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Biotech also pays a lot less.

If you look at current jobs such as "bioinformatics programmer", the salaries are somewhere between a biologist and a software engineer even though it requires more knowledge than either. Remember, biologists start off at around 35-50k/yr on jr. level, while SEs start at ~100k+.

Basically, it is better to be a junior level SE than a super senior bioinformatician.

This is certainly true. The reason why is most of the "bioinformatics programmers” come from a biology background and switch over to development. It is far quicker to go from biology -> cs than cs -> biology.

The real problem with biology is the salaries don’t rise as fast you become more more senior. Biotech is certainly not the easy way to wealth.

The time to market difference is huge in regular software than research. Also, the risks of failure are very high. So incomes are cut, except with blockbusters where the rewards flow to the top.

One reason I am glad I got out of biotech.

I'm in biotech now, thinking of leaving due to generally poor pay conditions in the industry. Have any other reasons to share?

I flat out had a tough time finding a job in biotech. I landed one and the pay was 30% less than what a regular tech company was offering. I still want to be in science, but now it is a hobby rather than my sole source of income.

Ah, I also had a hard time finding a job in biotech, despite having some solid referrals and awful offers. Settled for a mediocre offer as a result.

Anyways, thanks for sharing.

Biomedical informatics is often better compensated and has more potential for venture opportunities, but it also often requires a CS PhD, an MD, an ability to code and entreprenurialness in the same person... That's at least a decade and a $300k+ investment.

In think it might take you a tad longer than 10 years to get both a CS PhD and an MD.

It seems biotech is sorta where software was in the 80s (AFAIK dev salaries weren't so great till at least the mid 90s). I suspect in a couple more decades it will be quite a lucrative profession.

"even though it requires more knowledge than either"

It doesn't usually require more knowledge. The majority of bioinformaticians that I know are pretty poor coders, and know just enough Perl or Python to count some stuff up an do some stats on it or plot a graph. They have no idea how to set up a server, write maintanable code, design a database well or keep a website secure. Obviosuly there are exceptions, but the majority that I have worked with are junior level programmers with knowledge of biology.

"knowledge of biology"

I am not sure you fully appreciate what " knowledge of biology" means. As a programmer who did a lot of molecular biology in the past - doing molecular biology is not simple. The learning curve for a lot of biological topics - especially the ones that get close to organic chemistry is really steep.

This is something that I have seen people with a CS or EE background really underestimate. The amount of biology information you need to keep in your head is huge. There is not a core of day-to-day knowledge that you can use and look up the rest when you need, you need to keep all of it in your head just to get the most basic things done.

Its the same everywhere.

Programmers underestimating the value/knowledge of accountants/lawyers.

Businessman assuming tech work is simple.

People by default assume what looks simple for someone to do must be simple - even if they spent 10,000+ hours practicing it.

I have a batchelors in Molecular Biology, so I have a reasonable idea.

Depends on the person. Basically, to be good at both is a lot harder than writing a web app.

A lot of bioinformatics is implementing elaborate algorithms in a memory efficient way.

Things like

- approximate string search using modified suffix trees

- some new variation of the EM algo using a new optimization strategy

- the whole protein folding field where many implement the simulations on a huge array of GPUs. Here you not only have to know how to write efficient code on GPUs, but also be an expert in parallel algorithms if the algorithm you are implementing is not embarrassingly parallel.

However, these algos will be thrown out in a month so they don't care about maintainability or uptime.

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