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Two remarks from an old AI guy here. (1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted

Two remarks from an old AI guy here. (1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted person you are trying to hire. 'Everyone else is doing it', 'all our clients have data, so selling them data-sciency projects shoudn't be a problem', 'Hire a first DS person, to have them start a brand new DS division' are things that probably are doomed to fail. If you weren't selling classic Data Analyses/Intelligence services before, chances are you are lacking the foundation in your sales and support staff, and maybe even your customer type, to be able to embed this into your company and into a new line of business. The risk your prize new employee will walk to greener horizons to work with peers on exiting real projects is high. (2) Know where you are heading. If you just want to embellish your 'normal' projects with some gimmicks from the online 'Cognitive Services' api's offered by the various vendors, then yes, sending some of your regular staff on a quick 'boot-camp' style course will probably work. You don't need a 'data-scientist' for that. And if you hire a real one for that type of work he'll probably walk within 2 weeks.

tixocloud 8y ago
Sage advice. There is a significant misconception on what a data scientist does and many roles don’t seem to truly bring out their potential. Most get grouped into doing everything around “data”.
maxxxxx 8y ago
Agreed. From what I can tell the data scientists at my company pretty much are running a few SQL queries with simple stats. They also seem to have a high turnover. It looks like they got hired mainly because the company wanted "data science" without knowing what it means.
amrrs 8y ago
That's a very good advice. That expectation and reality mismatch causes a lot of problems and that's also well captured in this article. https://towardsdatascience.com/why-so-many-data-scientists-are-leaving-their-jobs-a1f0329d7ea4 https://towardsdatascience.com/why-so-many-data-scientists-a...
apohn 8y ago
>(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted person you are trying to hire. 'Everyone else is doing it', 'all our clients have data, so selling them data-sciency projects shoudn't be a problem', 'Hire a first DS person, to have them start a brand new DS division' are things that probably are doomed to fail. I'd add that if you have all these conditions and still want some hope of getting value from your Data Scientists, give your data scientist(s) a period of time (6 - 12 months) where they can discover how they can provide business value. Accept that during that period they may not provide the value you are hoping for. Support them if they recommend changes (e.g. different ways of collecting or processing data) Lots of middle/upper managers want to hire a team of Data Scientists and want them them to deliver high business value from day one. They want a magic black box to solve their business problems. This results in exactly what you describe - constant fires, games, gimmicks (that look good on PowerPoint), and good people eventually quitting or getting fired. On the other hand, if you give them 6-12 months to interact with different teams and identify relevant business problems, they'll find ways to provide value. Or, worst case scenario, they'll tell you (maybe by all quitting) that your problems aren't data science problems and the money you are spending on them is best invested elsewhere.
- crispyambulance 8y ago
  > if you give them 6-12 months to interact with different teams and identify relevant business problems... I fully agree with your points BUT 6-12 months of self-directed, open-ended discovery just ain't gonna happen. This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Something similar happened ~10 years ago with the advent of "BI" (Business Intelligence). It was nothing more than folks realizing that their corporate databases have useful stuff in them and they could query them in an ad-hoc fashion _outside_ of the applications that typically use them to provide "business value".
tootie 8y ago
My company opened up a load of AI/ML roles with absolutely no pending projects for them. CTO literally said once they are hired, they will find opportunities.

tixocloud 8y ago

Sage advice. There is a significant misconception on what a data scientist does and many roles don’t seem to truly bring out their potential. Most get grouped into doing everything around “data”.

maxxxxx 8y ago

Agreed. From what I can tell the data scientists at my company pretty much are running a few SQL queries with simple stats. They also seem to have a high turnover. It looks like they got hired mainly because the company wanted "data science" without knowing what it means.

amrrs 8y ago

That's a very good advice. That expectation and reality mismatch causes a lot of problems and that's also well captured in this article. https://towardsdatascience.com/why-so-many-data-scientists-are-leaving-their-jobs-a1f0329d7ea4 https://towardsdatascience.com/why-so-many-data-scientists-a...

apohn 8y ago

>(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted person you are trying to hire. 'Everyone else is doing it', 'all our clients have data, so selling them data-sciency projects shoudn't be a problem', 'Hire a first DS person, to have them start a brand new DS division' are things that probably are doomed to fail. I'd add that if you have all these conditions and still want some hope of getting value from your Data Scientists, give your data scientist(s) a period of time (6 - 12 months) where they can discover how they can provide business value. Accept that during that period they may not provide the value you are hoping for. Support them if they recommend changes (e.g. different ways of collecting or processing data) Lots of middle/upper managers want to hire a team of Data Scientists and want them them to deliver high business value from day one. They want a magic black box to solve their business problems. This results in exactly what you describe - constant fires, games, gimmicks (that look good on PowerPoint), and good people eventually quitting or getting fired. On the other hand, if you give them 6-12 months to interact with different teams and identify relevant business problems, they'll find ways to provide value. Or, worst case scenario, they'll tell you (maybe by all quitting) that your problems aren't data science problems and the money you are spending on them is best invested elsewhere.

crispyambulance 8y ago
> if you give them 6-12 months to interact with different teams and identify relevant business problems... I fully agree with your points BUT 6-12 months of self-directed, open-ended discovery just ain't gonna happen. This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Something similar happened ~10 years ago with the advent of "BI" (Business Intelligence). It was nothing more than folks realizing that their corporate databases have useful stuff in them and they could query them in an ad-hoc fashion _outside_ of the applications that typically use them to provide "business value".

tootie 8y ago

My company opened up a load of AI/ML roles with absolutely no pending projects for them. CTO literally said once they are hired, they will find opportunities.