Hacker News Reader

Comment by martingoodson | Hacker News Reader

Learning all this is not really that difficult. No more difficult than a biochemist training in subjects as diverse as organic synthesis (making stuff in test tubes), Raman spectroscopy (prediction of chemical structures using vibrational signatures) and DNA sequencing (computational analysis).

It's only because data science is much newer than biochemistry as a field that it seems beyond the grasp of an individual. It's perfectly possible to learn (and to teach) all of the things you've mentioned.

And what has pay got to do with it? Since when is pay correlated to how much you need to study (see, for example, musicians)?

View on HN

Data science is a role, not a field. It's similar to but wider than the applied statistician role that is well-established in many fields of research.

You have a background in one field, but you are working to solve problems in another field (e.g. biochemistry). To do that, you must understand biochemistry well enough to be able to contribute. You are probably far from the best biochemist in the team, as you were hired for your methodological skills. In order to solve the problems, you may need tools from a number of fields, including statistics, machine learning, software engineering, data engineering, mathematics, and theoretical computer science. No matter which field your original degree was in, it's insufficient in both depth and breadth. You must keep learning new things and rely on others with complementary skills.

I work in bioinformatics, which is basically a more established flavor of data science. I have worked with people from a variety of backgrounds from electrical engineering to genetics, and everyone has had obvious gaps in their skills. Except maybe one or two people, but they are world-famous experts who are unnaturally curious about everything.

Pay has a lot to do with it because if you can switch to an engineering role (SWE or Data Engineer) and have more focused responsibilities and a higher salary then that's what most of them will do.

Although given the demands made for a DS role are often unicorn-level I don't even think increasing pay would help.

The parent comment says ‘while being paid the exact same as someone on the SWE or PM track.’ Not ‘less than a SWE’, as you imply.

Why should a data scientist be paid more than a SWE? Because they have to learn several different topics? That is not such a big deal in my opinion (I work as a DS).

This language of ‘unicorns’ has been highly damaging to the field. There is nothing magical about a job which requires a lot of varied technical knowledge. Try looking at a syllabus for some other scientific subject. It’s fairly normal.

I work as a DS as well. I don't think there's such a thing as "should be paid more" - the market shows us that SWE's are more highly valued presumably because there is more demand for those skills.

However, this will lead to people migrating from DS to DE and SWE roles if the compensation is relatively better. Yet we see articles about a 'shortage' in DS when they just aren't paying as much as a similar skill-set can get in a different role.

> the market shows us that SWE's are more highly valued presumably because there is more demand for those skills.

I think it's that it's that a tech company can more consistently make money from a SWE than any other role. You can always roll together an app and sell it. For every other role[0], you provide value to the organization, which eventually makes its way to the customers.

This is why the software bootcamp grads have fared better than the DS bootcamps (and ML bootcamps). A company can get a lot of value from a pretty crummy SWE and is willing to pay for it. A crummy Data Scientist, not so much.

[0] Sales is also similarly direct, depending on the industry. They enjoy a similar status.

> There is nothing magical about a job which requires a lot of varied technical knowledge

It's magical when the varied knowledge is orthogonal.

Having strong business sense AND being good at coding AND having stats/math skills is most definitely unicorn level.

This is an unserious comment and the worst kind of gatekeeping, as it only applies to the shallowest definition of "learn" - perhaps "remember" and "understand" on Bloom's taxonomy.

Most biochemists, like most professionals in any field, are dilettantes in 99% of the field - it's the difference between reading a French cookbook and being Paul Valéry. They specialize and the rest of what they have learned rusts and sprouts weeds and is useless when viewed under the lens of applied knowledge.

And as the OP noted, your "all this" to learn includes (no offense to biochemists) probably the most multidisciplinary set of skills in any field: communication, business analysis, psychology, statistics, computer programming, hardware and network topology, data engineering, domain knowledge, often a deep background in one of the hard sciences ..

Of course it's not beyond any individual - Renaissance Men and Women do exist, but to suggest it's "not that difficult" is an uhelpful myth.

Reply on news.ycombinator.com