How many data scientists really do theoretical investigation? From what I see not too many. It feels a little like looking only for devs who can write complete operating systems and then having them write CRUD apps.
90% of data science time is spent sourcing and then cleaning the data. 10% is spent preparing and delivering reports to stakeholders. The ML time is a rounding error. The job in which you are magically given a pristine dataset and can just tweak ML models all day doesn’t exist.
Recruiter here. Data Science is one of ~3 roles I generally don't recruit for. Not because Data Scientists are super hard to find, but more because companies seem to all want data scientists and all want them to do completely different things and none of them really understand those things. Makes for a pretty lame hiring experience unfortunately. :(
You think you've got it bad, imagine how it is for us candidates.
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
Saying ML is "just linear algebra and calculus" is a gross simplification. As a dev, you can pick up some popular ML frameworks and learn the basics relatively quickly. The difficulty in this field comes from the amount of theoretical knowledge you need to interpret your results. All these data science bootcamps/learn quick schemes are like teaching a blind person to drive a racecar. He can work the pedals and the st…
Exactly this. Implementing algorithms that work on well-defined clean data isn't difficult. Working with naive problems where the right algorithm isn't clear or can't be directly implemented is difficult. In many cases cost of implementation is significant. You can't just apply a model to millions of points of data and blindly expect to get anywhere without fundamentally understanding the data you're working with first. That's what separates data scientists from data engineers, and the ability to take a naive problem and work with it to a conclusion in a manner that's understandable both from a technical and business point of view is exactly why having a PhD is typically a barrier of entry because that's all academics in data science do all day!
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
Data science is like security: it's very easy to come up with something that looks really good but is horribly broken in subtle ways that you won't find until it's too late. I think there's a lot of value in having lots of people be literate at it, but part of that literacy is understanding that it's a hard problem and that it does need attention from dedicated specialists.
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
That's very valid because Recruiters are dumb confused. I'm an undergraduate engineer with decent experience in Analytics and my resume doesn't even get into some company because I don't have Masters while my friend who did an MS in Data science from UK is also not getting a job because she just pivoted her career from Mainframe to data science with that MS and they don't want a fresher. Hence you are left with a minimal talent pool that gets to hop here and there increasing their market value while the demand doesn't reduce and at the same time with this fancy word PHD every company is ants to get them or at least an MS just for God knows what reason. In an ideal scenario, You are just exploring data and getting actionable insights ( I'm not talkign about publishing paper nor programming self.driving cars). I'm an average guy knowing R and Python with Pandas and Scikit learn. Anyone who can connect the dots can learn these tools and become a good data scientist, but the blind belief of these recruiters just kill those hope and look for the hardly available job hoppers. Such a paradox self created by them.
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
The Data Scientist "shortage" is another scam by the big tech companies to drive down wages. My boss had hundreds of applications for the position he was looking to fill and the majority were so well qualified that it was basically a coin flip between multiple PhDs with great resumes.
Tech worker's arrogance reminds me of "made in the USA" factory workers who thought their jobs were safe. If people don't start pushing back wages will be pushed down to global equilibrium. Based on how things are going I'm predicting neo-feudalism thanks to the joys of open-boarders globalism putting all power into the hands of corporations.
Recruiter here. Data Science is one of ~3 roles I generally don't recruit for. Not because Data Scientists are super hard to find, but more because companies seem to all want data scientists and all want them to do completely different things and none of them really understand those things. Makes for a pretty lame hiring experience unfortunately. :(
I know a mid-level manager at a Fortune 100 company. Somewhere up the management chain, someone read an article about how data science is the hot new thing that your company has to be doing or it will have missed the boat. So word was sent down that their next project was to do data science. So now they are launching a data science project to analyze their business data, which this manager is overseeing. They're hiri…
That's unfair. "Not missing the boat" is a natural need for a company. Everyone with an MBA has studied companies that missed the boat. Throwing some money at various new fads isn't necessarily irrational.
My wife will be attending a university based data science boot camp. She has spent 12 years as s microelectronics process engineer. Seems a dead end job and can’t break the $100k mark. Hopefully it will be worth it.
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
Well - there _is_ a bit of Student's Paradox involved in requirements-gathering for machine learning or analytics, and it's not always apparent that you're using the right algorithm for the problem or what specific metrics you actually need to be optimizing. Most of the time when I've gone through User Story collection processes, the actual end-state users describe is vague, not based on hypothesis testing, and not s…
I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
I think it might be a easier path if you apply for "data engineering" jobs and convince internally to give you a chance at data science. Get data science on your resume and look for next job.