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Use Machine Learning to Find your Next Job

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A wise man once said, "I got ninety-nine problems," and I can relate to that in some sense. Because on a day to day basis I run into problems; I run into things that aren't as easy as they should be or things that I want to be better. And I suspect that because you are all in this room on a Saturday you also have problems, you also run into things and want to use software to solve them. Today I want to talk about ways that we can use software to solve our problems and specifically to give those software solutions some intelligence using data.

Now the motivating example and the one for which this talk is titled is a job search helper thing that I made. Basically what happened was a few months ago I was passively job searching, which is to say that I wasn't actively out there knocking on people's doors and handing out résumés, but I was curious to see if there were any particularly excellent jobs in my area. I went out and tried to sign up for different job alert things that would give me the coolest jobs and I couldn't find anything that did exactly what I wanted it to do. Like any good Engineer I decided to build it myself. I built this little email newsletter that I would send to myself every week that essentially had the coolest sounding jobs in my area. I could go through and just review those jobs. It was basically a way to filter out a lot of the noise. And so we're going to use this as sort of a case study today, to talk about a process that I've gotten to use a few times that has worked for me. I wanted to share it with you all to hopefully provide some value in your own lives.

So this is how we're going to be doing that, the astute among you have probably noticed we are currently in the introduction. After that we're going to talk about asking the right question; basically phrasing questions in ways computers can help us answer them. Once we do that we'll talk about ways to gather the data, and then we'll analyze the data. Finally, we'll deploy the insights that we gather, and here I don't mean deploy in the sense of we're going to put the code on a server somewhere. That part is interesting, but more relevant to this discussion is how do we get a number to be something interesting to a person and express in a way that people can understand it.

So as for me I'm originally from this part of California (Bakersfield, CA). It's the really boring part, but it was a great place to grow up, and then I left and went to Baylor University where I studied Computer Science. I really enjoyed my time there, and while I was there I sort of got bit by the data bug and got to do some research in an autonomous drone lab that was really exciting, do some research with collaborative filtering (which is a recommender systems kind of thing) that got me started down the path of the thing that I ended up building for this. Another relevant thing I got to do while I was there was I taught a computer sign language which was really fun. And then over the summers I would go do internships in various places and learn a lot about software and good engineering practices. I tried to unify this all together, and then after I graduated went and started doing some data engineering work, and now I actually work at Indeed (which is the world's largest job site). Interestingly enough literally everything that I'm going to talk about today is completely unrelated to my job there (other than the fact that I do data stuff there too) but all the code that I wrote for this I actually wrote while I was at my prior company. But the most important thing on this map is the fact that we're all here in this room today to talk about this stuff, and I'm really glad that you are all able to come and honored by your presence here today.

So that's all the boring stuff–let's get in to the cool part! The first step in this process is to have a problem. This is the easiest step because it's the one that just comes naturally. It's the one that you bump into on a day by day basis. For me, I bumped into the problem that job alerts are too noisy. There's too many jobs for me to reasonably look over in a short amount of time. I've also run into problems where I was trying to figure out how to get my energy bill lower or trying to figure out how to get home from work faster. Once you have a problem the next thing you're going to want to do is start to think about solutions. In order to do that, you need to understand the ways we can ask computers questions and get useful answers back from them, which leads us to the fun buzzword of the day: Machine Learning.

If you have heard the phrase "Machine Learning" please raise your hand. Alright, yeah it's a buzzword we've all heard. If you feel like you have used machine learning in a substantial or interesting way, raise your hand. Awesome, some more great hands. Alright, one last question: if the phrase approximation-generalization tradeoff means anything to you, please raise your hand. That's fine we'll talk about it later, I just wanted to know what to go into. Thanks for your participation there.

So what is machine learning? There are a few different things that comprise it, and I'm going to talk about a subset of it today. There are a few kinds of algorithms broken into a lot of different categories, but this is good enough for today. There's a type of algorithm called a supervised algorithm in which you are basically feeding training data into a computer. That training data has a number of features that are like input values and then a number of output variables. Here on this graph what you see is that the X axis is age and the Y axis is net worth. The example problem here is basically: I'm a bank and people are coming to me asking for a line of credit. I'm trying to decide whether to extend them a line of credit or not. One way you could theoretically do this is to just look at your past history and say, "Okay when people have come to us in the past and asked for credit, how old were they? What was their net worth? And did we extend credit?" That's what this graph is displaying: the age and then the net worth. The plus or minus you can think of as a one or zero of whether or not we extended them a loan. And then the machine learning part of this is basically draw a line through this data. It doesn't have to be a line but a line works for this so we're just drawing a line. And you'll see here that because in the past we had someone who is ninety and did not have very much money who came to us and asked for a loan and we rejected them, if someone who is similarly aged and similarly wealthy came to us, they will be below this line, so we would not extend them a line of credit. That is an example of a classification problem, because there are classes involved. There is a "positive" (we did extend them a loan) or a "negative" (we didn't extend them a loan) kind of question. Classification is great for when you're trying to find out what kind of thing something is.

Now, if I were a bank trying to decide how much credit I should extend to a person, I would have to use a regression algorithm. Regression is very similar to classification in that you still have a number of input features and an output of some sort. Here it's a little bit confusing because here only the X axis is an input. On this last slide both X and Y were inputs and then the plus and minus was the output, but here we're just saying that the X axis is our input of net worth. Someone comes up to us and they say, "I have five hundred thousand dollars, how much of a loan can I get?" The "X" symbols aren't significant (they're just to mark position), but you can see (for instance) someone who had very high of a net worth down near that bottom left hand corner only got a loan of a thousand dollars because that was a more risky person (for instance). As a bank, I have all this historical data, and I can train some sort of algorithm that would again draw a line, and we could then say if someone comes up to us and has a seven hundred fifty million dollars net worth we can look at where they would land on the X position of the line. Then the Y value then would be the size of loan we extend them. Here it looks like seventy five thousand dollars or something. So that is another kind of supervised machine learning algorithm.

There are also unsupervised algorithms. One such algorithm is called clustering and in clustering you have a bunch of data points, and I apologize that this is not the same example, but you can imagine that each of these dots is a customer. The X axis could again be their age and Y axis could again be the net worth. Maybe it's computationally prohibitive to do the calculation on the full data set, but you could theoretically cluster people and say there are (for instance) eleven kinds of people. Then, depending on the kind of person you are, we could make a decision based off of that. In clustering, you're not trying to get a specific output, you're just trying understand the data better. It's often useful as a preprocessing step. You might again have someone come in and they have a certain net worth and certain age. You could say this person is really similar to this other kind of person that we usually extend a credit to, so let's extend credit this person.

There's a lot of other stuff in the field of machine learning that is time prohibitive to talk about today. So this third of this slide is here to prevent angry tweets because there's a lot of stuff that is really interesting that just doesn't quite fit in today.

So once we know the kinds of questions we can ask a computer, we can figure out a way to phrase our question. In my example, I'm thinking, "OK, job alerts are too noisy for me. What do I want? I want to know what are the coolest jobs. OK, well maybe I can ask a computer. Given my input of a job title, give me the output: does it sound cool or not (just as a one or a zero)." And that's a way we can phrase our question in a way a computer can actually help us with. So now that we have this formulation of our problem, we can jump into gathering our data.

There's a lot of data out there, and the best thing to do is just search for it. Go out and Google it. For instance, one time I was trying to determine my energy usage, and I thought it was probably going to be correlated with weather. I was looking for weather data, and there is this government agency called the NOAA that has a big weather data set that you can just download and use. And so it's very likely that you'll get out there and search for something and there's already a government agency whose job it is to collect this data, which is really exciting because it means you then have to do less work. In the case where you don't find something that already exists through searching, you can also try various websites. data.world is one, Kaggle datasets also has a similar feel where they have a bunch of existing datasets about usually more broad things. They don't tend to be a specifically relevant if that makes sense, although they'll have things like crime data on their website. That may or may not be useful to you, but if if you're trying to figure where to buy an apartment and you want to look at crime statistics, that dataset might already exist.

So you may or you may not find the data you need, and if you don't you're going to have to create it at some point. I like using spreadsheets for this because I can have them on my computer and I can have them on my phone, and anywhere I am I can collect more data. Other than that there's a tool called If This Then That that can be useful, especially when you're collecting data on your own personal habits. For instance, when I was trying to find out when the best time to leave my office was to minimize my commute time, you can get a little button that IFTTT will make for you where when you click it, it'll log your location and the current time to a Google Sheet. So what I would do was when I left the office, I would press the button then when I got home I would press the button again. In that way I could calculate how long it took me to get from my office to my home and at what time I left. And then I could have all this data about, okay you can leave at this time (that's your input value) and it took you this long (that's the output value). Now I know that Google Maps can also do this for me, but I'm a nerd and we are at a developer conference so I think it's fair to over-engineer something.

Beyond that, web scraping is another great tool. What this basically is downloading a website and picking out the important bits. There are some legal things here, and I am not a lawyer, so do your own lawyer stuff but an important tip is that when you're trying to scrape a website, look at their robots.txt. Whatever a website you're on, take the domain name and put /robots.txt and it'll have a listing of thing of basically the places you're not supposed to go if you are a computer. Please obey that and you're probably fine, but again I'm not lawyer and this is not legal advice.

And maybe the case is that you combined these two methods. That's exactly what I am doing in this project. I web scraped a bunch of job titles, and then when I had spare time on the bus or something I could click through the links on my phone, read the description and come back to say whether or not the jobs sounded cool. Columns A through D here are existing data and then column E is the augmented data that I'm creating myself.

You're going to need to clean this data. I heard someone speaking at a conference and they said, "Fifty percent of data science is cleaning data." And when he got done he had all these people coming up to him that said, "That's ridiculous! At my job it's eighty percent!" There's two tools that I highly recommend if you're in the Python ecosystem: Pandas (which does a great job of loading data into a tabular format in memory). I've heard it described as "in memory SQL". And then scikit-learn has some stuff built in to massage data into a format that computers can more easily understand that we'll get to in a moment.

Now you may remember this graph from before that had numeric data. Computers are good at numbers; computers aren't as good at words. You may think, "Well, if I had someone's age and net worth, I easily see how those are just numbers. But for something like a job title, that is different. That doesn't feel like I can just type that into a computer and have it fit that into a graph because I I don't even know how that mapping would work. And so we want to introduce something where we take as input the job title and as output whether or not it sounds cool, then turn it into some set of numbers. The great thing is that when you run into a problem like this there are a wealth of giants whose shoulders you can stand upon. You can just Google "text representation for machine learning" and out will pop this probably. This is the idea of word count vectors or "bag of words." Essentially what's happening here is you'd take all of your job titles and you keep track of either all of the words that were used in every single one or maybe the three hundred that are used most frequently. Then you stack them all up, go through each job title, and count that how many times each word occurred in the given job title. So we can see for this first job title "Senior Web Applications Developer" that the word "Engineer" occurs zero times in this job title and the word "Web" occurs one time, etc. I'm not going to bore you by enumerating this matrix but you see how this process works. "Word count vectors" is a fancy way of saying strings of numbers that count up how many times a given word is in a given job title, and that gives us exactly what we're looking for. We can now go from the job title and the output variable (of whether or not it sound cool) to this set of numbers where these first ten numbers are the number of times a given word occurs (so maybe that first number is "senior") and that last number there is a one because that job title sounded cool to me.

And so now can start actually analyzing this data which is great. There's a few tools that I recommend for doing this kind of work. Jupyter is really great; it's an interactive programming thing. Essentially you run it on your computer, and you can load a browser up and do stuff, and it'll show you the output of it immediately (which is super helpful). It's nice being able to see what the data looks like and it's nice to be able to understand what your next step should be. You can also do neat things like drawing graphs, such as the one shown in this screenshot. The maintainers of this project have put a ton of work to make it basically the de facto, interactive, iterative programming tool for data science and data analysis people who are using Python. I spend a lot of my day in Jupyter Notebooks at work.

I also definitely recommend Pandas and scikit-learn (which we talked about earlier). It's nice to not have to re-implement all these algorithms from scratch because other people have already done it for you.

So this is just a little code example to show you how easy this kind of stuff can be. Often times we talk about machine learning and it sounds really scary and foreign. But when you actually look at the code you'll realize this is something anyone can do. This is not complicated, it's just a little bit of understanding how these algorithms work and then reading some documentation. I often call things X and Y because I'm just used to that nomenclature so I take our job titles out of the dataset that I have and I put them into this X matrix. I take whether or not this sound cool and put that in this Y vector. The next line is a CountVectorizer (it just does that word counting thing that we were talking about earlier) and then you can just say, "OK, take this matrix and turn it into the word counts." Then you create a model, you fit the data to it and then you can just call .predict on it and it will give you this beautiful array (that I have here bolded) that says, "Job zero did not sound interesting, and then job four does sound interesting." You can get this all out very easily; it's not a lot of code to get a lot of value.

The thing you want to do after you've been able to gather your data is just do the simplest thing that could possibly work. There's good reasons for doing this. Earlier, no one knew what the Approximation-Generalization Tradeoff was. My hope is that you are about to learn. The idea here is that the better your algorithm approximates the input dataset, the worse it is going to do at generalizing to data that is outside of your input data. That's a little hard to just say and have it be understood, so I made a little graph that I think will help. In the process of making this graph, I first generated a true dataset. You can see here the blue line and this basically says that when we enter zero the value that comes out is zero, and then at the far right end of the scale when we entered ten we expected negative twenty two out. It is a very simple function that we're trying to estimate with our machine learning stuff (it's purely for illustration). And you can see then I generated ten data points from this blue line by just adding a little bit of noise to each point because the real world is fairly noisy for a number of reasons. And then I fit two different models. The red line is a nearest neighbor model (which is a more complicated model than the linear regression model,) and you can see that it does an excellent job of representing the dataset that we gave to it. It is matching perfectly at every single data point there which is great, but you can also see that it is not very close to the truth. If you were to draw more data points from the same truth we would basically find that the red line isn't doing a good job of generalizing to data that it has not seen yet. This would be like if you were taking a practice test for a math class and all you did was memorize the right answers to each question. You would do great at the practice test, but once you got to the real test you would do horribly (because you don't know actually know how to do any of it, you just know the right answers).

By contrast linear regression is doing a much worse job of approximating the input data set. If you see for X value equal zero it's roughly five units underneath that data point and similarly from the range like two, three, four it's not doing a great job either of approximating the input data. However one thing that you'll notice is that line on average looks a lot closer to the truth than the red line does and the reason is that it is better able to generalize to out of sample data. What we can see here is that the red line is doing what is called overfitting, which is when you learn too much of the noise in your algorithm. And that's a real problem that is easy to run into, especially when you're using complicated methods. There's a lot of really interesting stuff on Hacker News that will have you believe that you should use TensorFlow and PyTorch and all these really interesting and exciting deep learning frameworks. And they are all very interesting and very exciting and have great applications, however they are often more prone to overfitting than some simpler models. So it's great to just start with something simple and you can move on from that if you need to.

Another benefit is it often just easier. It's a lot easier to get scikit-learn running on a Mac or a Windows computer than it is to get PyTorch or TensorFlow running. Aside from that, when you're iterating through this development process it's a lot easier to have something that trains really fast and you can try out a bunch of different ways of representing your data or different ways of sampling it (and have that be fast) rather than something that takes a long time to train. In practice with this means is just start with something simple, linear regression and logistic regression are both great models that are good places to start and you can use them both for regression or classification.

So we've gotten to the point here with this that we're in the deployment process. By this I mean that we have these numbers right? We got those numbers out of our model (the zero and the one), but if I were to just look at zero, one, zero, one, zero, zero, one, that doesn't do anything for me. And I also don't want to have to run that code myself every time so one thing that I was thinking (because I am my own user, and I can kinda read my own mind) was I wanted to build this email which you already saw earlier (spoiler alert, sorry) but basically the thought is I don't want to run this thing on my own and I want to get just the relevant and interesting jobs. So what I did was just put it onto a server that I rent and have it run every week, and then send me just the jobs that are interesting. It formats them a little nicely and puts them into an email and sends them off to me. And this is a good thing to do is just build something simple and ship it. Get it working, because the next step here (and if you've gone to any of talks that people have been doing about agile practices this should sound familiar. Because just because we're using data in the software does not make it any less software) is to test out our product. We still need to test out our product, we still need to try it on actual users, we still need to figure out what doesn't work and what does work, we still need to iterate and we still need to iterate again and try something new. And we need to iterate even another time, and we just keep moving and keep trying new things until we get to something that works really well.

So to summarize because that was a good amount of things:

  1. Step one is to have a problem. Find something that you want to solve that sucks in your life
  2. Once you have that thing, phrase it in a way that a computers can help you answer it. There's a lot of things computers are good at. If you have a problem that you can rephrase as a "how much?" or "what kind of?" problem, those are really great candidates for a machine learning application.
  3. Once you do that, you're ready to get some data.
  4. Try the simplest thing you possibly can and see how well that works. You can test out and iterate from there. But it's really important to get this in front of users and to just try new stuff.

So that is the gist of what I have for you today. If you want to learn more, there's a text book called Learning from Data that is really excellent. It does a good job teaching machine learning in a good way. A lot of times when people talk about machine learning, it comes off as a bag of tricks, but this book does a good job of helping you understand some of the theoretical underpinnings that help make these algorithms work. And then from a less academic but more practical side there's a blog called Practical Business Python where the author talks a lot about data visualization and how to do useful stuff with Pandas and it's extremely useful when you're trying to learn this stuff.

Also sponsors are good. We like them. If you are looking for a job I'm sure some of these people are hiring, and we're very grateful to have them here. I would be remiss not to thank my employer (Indeed) because they paid for me to be here so thanks for that. Other than that I hope that you've gotten something out of today and would love to meet you all after. If you have any feedback: if it's negative please email me; I would love to hear what I can do to make this better. If you have any positive feedback please tweet it.

I hope you've gotten something out of today and are better able to go solve your problems.