I've been very interested in data-mining and machine-learning for a while, partly because I majored in that area at school, but also because I am truly much more excited trying to solve problems that require a bit more thought than just programming knowledge and whose solution can have multiple forms. I don't have a researcher/scientist background, I come from a computer science background with an emphasis on data analysis, I have a Master's degree and not a PhD. I currently have a position related to data analysis, even if that is not the primary focus of what I'm doing, but I have at least some good exposure to it.

As I was interviewing some time ago for a job with several companies, and got to talk with a few recruiters, I found a common pattern that people seem to think that you need to have a PhD to do machine learning, even if I may be generalizing a bit too much (some companies were not really looking especially for PhDs).

While I think it's good to have a PhD in that area, I don't think this is absolutely necessary. I have some pretty decent knowledge of most real-world machine learning algorithms, have implemented most of them myself (either at school or on personal projects), and feel pretty confident when approaching problems involving machine-learning / data-mining and statistics in general. And I have some friends with a similar profile who seem very knowledgeable about this also, but also feel that in general companies are pretty shy about hiring in data-mining if you're not a PhD.

I'd like to get some feedback, do you think a PhD is absolutely necessary to have a job very focused in that area?

(I hesitated a bit before posting this question here, but since it seems to be an acceptable topic on meta, I've decided to post this question on which I've been thinking for a while.)

I believe actually the opposite of your conclusion is true. In The Disposable Academic, several pointers are given about the low wage premium in applied math, math, and computer science for PhD holders over master's degree holders. In part, this is because companies are realizing that master's degree holders usually have just as much theoretical depth, better programming skills, and are more pliable and can be trained for their company's specific tasks. It's not easy to get an SVM disciple, for instance, to appreciate your company's infrastructure that relies on decision trees, say. Often, when someone has dedicated tons of time to a particular machine learning paradigm, they have a hard time generalizing their productivity to other domains.

Another problem is that a lot of machine learning jobs these days are all about getting things done, and not so much about writing papers or developing new methods. You can take a high risk approach to developing new mathematical tools, studying VC-dimensional aspects of your method, its underlying complexity theory, etc. But in the end, you might not get something that practitioners will care about. 

Meanwhile, look at something like poselets. Basically no new math arises from poselets at all. It's entirely unelegant, clunky, and lacks any mathematical sophistication. But it scales up to large data sets amazingly well and it's looking like it will be a staple in pose recognition (especially in computer vision) for some time to come. Those researchers did a great job and their work is to be applauded, but it's not something most people associate with a machine learning PhD.

With a question like this, you'll get tons of different opinions, so by all means consider them all. I am currently a PhD student in computer vision, but I've decided to leave my program early with a master's degree, and I'll be working for an asset management company doing natural language machine learning, computational statistics, etc. I also considered ad-based data mining jobs at several large TV companies, and a few robotics jobs. In all of these domains, there are plenty of jobs for someone with mathematical maturity and a knack for solving problems in multiple programming languages. Having a master's degree is just fine. And, according to that Economist article, you'll be paid basically just as well as someone with a PhD. And if you work outside of academia, bonuses and getting to promotions faster than someone who spends extra years on a PhD can often mean your overall lifetime earnings are higher.

As Peter Thiel once said, "Graduate school is like hitting the snooze button on the alarm clock of life..."
