Stack overflow salary calculator shows a significant 50% premium over Developer salaries, all other things remaining the same. [1] Even though in my opinion the tool is flawed and actually significantly underestimates (stackoverflow underpays) salaries in SV/NYC. It is still a good indicator. The major issue is that Data Scientist is a very fuzzy term with it being applied to everyone from undergraduates with Stats d…
From my experience this year recruiting coming out of undergrad, for the top kids who do DS vs the top kids who do CS, the median comp is higher for DS but the highest comp packages come for CS. I wouldn't be surprised if this holds for more experienced people too.
People with acquired skills are plenty and not really up to scratch most of the time. So people who have these "acquired" skills have a high likely hood of being scrapped at the CV stage.
If you're serious about machine learning - build a blog or online repository of quality work and use that to get a job instead
Speaking for NYC, but I imagine silicon valley is similar. The supply-demand dynamics have changed a lot in the last couple years. I'd roughly break it out into two groups: people with work experience + strong software development skills, and those without. The first group is in higher demand than ever, and tend to add a lot of value to companies that really need it. The second group has gotten extremely crowded, esp…
I am currently studying Bachelor CS an am interested in ML/DS. How can i make sure i fall in the first group? I try to do some side-projects to expand my skill set and practice software dev. skills, but this whole thread seems pretty discouraging.
If your perspective of ML was based solely on the comments in this thread you'd think there's 0 opportunity in the field -- obviously not true.
The people who comment on these kinds of posts tend to always be incredibly cynical, and I am not sure why. They offer a limited perspective on the industry and you should not let it discourage you from your desire to learn and become an ML engineer. If I were an undergraduate CS student interested in ML/DS I would start by taking an intro course for ML and creating a side project you're proud of.
I hire machine learning engineers and data scientists. In my opinion there is a great shortage of truly qualified machine learning engineers. A lot of people are entering the market with a general knowledge of machine learning tools. These people should be considered analysts or product data scientists. When it comes to people that can build machine learning systems that work at scale, they are very rarely available…
Whats the time from hire to a built live production system you would expect a truly qualified engineer to be able to achieve?
Is there a market for competent developers without professional/academic experience in data science or machine learning? Perhaps just a MOOC or some Kaggle projects?
Speaking for NYC, but I imagine silicon valley is similar. The supply-demand dynamics have changed a lot in the last couple years. I'd roughly break it out into two groups: people with work experience + strong software development skills, and those without. The first group is in higher demand than ever, and tend to add a lot of value to companies that really need it. The second group has gotten extremely crowded, esp…
The problem is DS is really 2-3 different disciplines under one nebulous title. What you're describing is folks who are prototyping and productionizing models. That's definitely in short supply, but random STEM PhDs are in no way competitive for those roles unless they're coming from CS programs + have work experience in production engineering. But that's by no means all of the DS field. There are lots of DS jobs whe…
I was part of the Data Science team in my previous company. We mainly build models for production, but we also were responsible for generating both daily and ad hoc reports. We tried to hire someone to take over the reporting part, but we found out even that requires engineering skills. This role ended up to even more difficult to hire for because it's hard to find someone who has the engineering skills but wants to work only on reporting. Maybe if we had a dedicated data engineering team the story would be different.
I've only been hiring DS folks since 2012, but my experience matches what you've said exactly. The biggest differentiator I've seen is to be able to participate in actually building production quality systems vs being proficient enough in R or python to hack together a prototype on a very small dataset. The former kind of data scientists were very successful at our company, the latter, not so much. Both categories I…
That sounds weird to me. Does American PhD don't have to work for a few years at real companies as part of the PhD curriculum?
I am used to the PhD being an academic, not practice (trade) degree. This is true even in fields where the PhD is pretty much required for work in industry (medicinal chemistry, biology etc). And even in computer science where a PhD can even be a hindrance to getting an industry job, the PhD is pretty much university only.
I am curious what countries it works the other way around.
We hire applied ML/AI specialists. For me it's not just an understanding of mathematical concepts, but also being able to apply new ideas to new problems. This depends quite a bit on critical thinking, a good fundamental ability to analyze a problem and understand its parameters, then manage the logical operations required to deliver the feature and solve the problem. As for why I think it's on HN every day: I also l…
It seems that you made it reverse of what you wanted. As far as I understand, data scientists should start the exploration and developers should finish exploitation.
Interesting observation! Perhaps its a bidirectional graph.
I was thinking in terms of the development like this:
1. Observation made
2. Idea created
3. Software/Hardware made
4. Revenue achieved
5. Business parameters tuned using insights
6. Maximum profit achieved
Building a career around a framework is never a good idea. If you know your shit, it shouldn't matter what framework you're using. Theano and TF, for example, both make similar abstractions: graphs and numerical functions on top of the same matrix library, even. I would suspect someone could move between the two fairly easily. The problem is that a programmer can use TF/Theano/etc.'s built-in gradient descent functio…
If you know your shit, it shouldn't matter what framework you're using. And yet, what is the incessant drumbeat of most job ads, these days - even in data science? That's right: "N years in framework X"
> That's right: "N years in framework X"
I don't know where this meme comes from. Six out of the seven jobs/internships I've had (in SF, Seattle, and Toronto) didn't care about any specific framework or language whatsoever and still don't.
The problem is DS is really 2-3 different disciplines under one nebulous title. What you're describing is folks who are prototyping and productionizing models. That's definitely in short supply, but random STEM PhDs are in no way competitive for those roles unless they're coming from CS programs + have work experience in production engineering. But that's by no means all of the DS field. There are lots of DS jobs whe…
I was part of the Data Science team in my previous company. We mainly build models for production, but we also were responsible for generating both daily and ad hoc reports. We tried to hire someone to take over the reporting part, but we found out even that requires engineering skills. This role ended up to even more difficult to hire for because it's hard to find someone who has the engineering skills but wants to…
That is me. A decent developer who gets data and enjoys reporting, particularly the variety of hard problems that crop up.
I am happily serving a niche market with my own company and I suspect part of the difficulty in finding the skillset is that we can just start our own thing when we find a domain that we like.