Hey friends! If you’ve been paying attention to how the data processing industry has changed recently, you may find that employment has changed a lot lately. As many companies become virtual in the post-COVID world, many people are re-evaluating their current jobs for preferential treatment. (I hesitate to use the word “better” chances because “better” is relative.)
Although I am not in the job marketself, I thought I would write this post to all people who are considering a change in the work of computer science. I don’t think most resumes are very good because they tend to follow outdated “best practices”. Advances in technology have changed things so that places like LinkedIn allow a person to “vomit a word” throughout their work history, which means it’s no longer necessary on a resume.
Furthermore, I do not think that the old follow-up instructions captured this reality at all well: people are very irrational. We are all irrational, at least in some area of our lives. While we would like to think we are irrational when it comes to choosing the ideal candidate for the job, irrational factors tend to intrude subconsciously. Therefore, several of these tips have been written to take these irrational factors into account. To be completely honest, here is at least one tip that I personally find completely ridiculous. You will learn what is little.
Before we embark on these tips, I updated my own personal resume to help with a concrete example of how accepting these tips might look like. In addition to being a machine learning engineer, I also do graphic design on the side and used Affinity Designer create my resume. Affinity Designer is great, but like its sadder counterpart – Adobe Illustrator – it’s a tool designed for more professional graphic design use. Personally, I think it is worth considering using this software because creating a basic image for graphic design is very convenient. (For example, all of my blog’s title cards are created in Affinity Designer.) But if you want to keep things simple, I recommend a platform like Canva or EnhanCV.
Okay, let’s start with our tips!
Considering an earlier comment on how technology has changed the way people are hired, this is especially true for data science candidates. If you’re applying for a job today, chances are there’s a form-based application process or something that scratches information from your LinkedIn profile in addition to submitting a PDF-type resume. In addition, the employer is likely to assess your skills using coding, so it is no longer necessary to ensure that you get all the small skills and experience for yourself. Chances are that your employer will screen the entire stack of these resumes, so you want to keep things as short as possible. There is no reason that your resume should be longer than one page. And if you organize things like me, you can get a lot of information on one page!
Consider a traditional, average continuation. Most traditional resumes contain only black text on white paper. Now imagine if you hung the traditional resumes of all the applicants on a wall and occasionally included a plan like them. Standing 30 feet away, could you choose which sequel is mine? The answer is, of course, yes! At 30 meters away, there is no way to read anyone’s resume text (unless you have a view like Kotka), but you get a general feel about the person’s design. Here we begin to see people’s irrationality fade in. If you gave a hiring manager or recruiter a stack of resumes, they tend to fly through simple, old-style resumes, even if they represent highly educated candidates. They tend to stop at a specially designed sequel just because… well, it looks good. Again, that doesn’t make sense, but since I want to see you succeed, I would do you a disservice if I didn’t mention this. When we talk about irrationality …
Remember when I said in the introduction that there was one clue here that I find ridiculous? This is the exact tip I was talking about. Once again, referring to the irrationality factor, people unfairly judge people based on their physical appearance. You’ll find that I include my own self-portrait, and that’s just because I think my photographer friend definitely did an out-of-this-world job with my headphones. In general, I don’t consider myself a “conventionally attractive” person, so if I had just taken a selfie on my smartphone, I’m not sure if I would have included it. Also, I’m lucky to be a younger person, and while this is completely illegal, I’m sure there are some employers who shine a person’s resume if they seem to be older. Again, the fact that this is even a problem frustrates me without end, but I can’t change my mind about hiring people. I can only help you, friends, to compile your resume in an irrational world.
As we discussed earlier, a resume is really just one part of a whole set of things that encompass an employer’s views when evaluating potential candidates. Chances are that in a form-like app, your employer will ask for things like a link to your LinkedIn profile, but if it doesn’t make sure it shows up on your resume. Especially for computer scientists, I would like to make sure of the following:
- Your GitHub profile: Most technical employers are familiar with GitHub and the value it can offer a candidate to present their work as a code. I’m not going to cover it in this post, but rest assured that your GitHub profile is organized so that your portfolio clearly reflects what you want your employer to see.
- Personal websiteA: I’m honestly a little careful about this. I personally think that as long as you have solid GitHub and LinkedIn profiles, there is no need for a personal website. However, I would recommend that you get your own domain and that you at least have it refer to some type of a LinkTree. This requires little effort and allows the employer to move seamlessly anywhere you want.
- Other data science issues: I’m thinking about things here, like Kaggle races, or posting posts for example Towards data science. Someone with experience interviewing Fortune 50 machine learning engineer candidates can tell you that I have never personally interviewed anyone who has shown these extra things, and that’s okay. I may not expect to see these, but I can tell you that they would definitely be iced on the cake!
This is a very important tip for those who are just breaking into the field of data science. As a mentor in the computer science that encodes bootcamp, I’ve seen students struggling from there struggle to know what experiences to pursue in their resume. I think it’s important to place at least one or two of the previous positions just to show that you have general workplace experience, but if the last 8 jobs were all in restaurants, for example, I wouldn’t list them all to save room on your resume. As this extra room continues, I would instead like to highlight one or two personal science projects. When describing that project, be sure to include the following:
- Brief description of the project: Two or three sentences about the overall goal of the project
- How the project was successful: My computer science startup campaigns, the projects they usually pursue, have some kind of positive impact on the world, whether it’s directly from the company or “more open source”. I definitely encourage highlighting the successes of the project as it helps the employer understand that the project was a continuation in the field of computer science.
- Techniques used to support the projectA: This can simply be a list of things you use, including Python libraries, open source tools, cloud platforms, and more. For example, you could list something simply like this: “Techniques used: Pandas, Scikit-Learn, FastAPI, AWS SageMaker”
This is especially important because positions in data science can be quite confusing. Some stations are closer to general data analysts, where the station focuses more on data collection and data cleansing. Other positions may focus more only on building predictive models. Others, like my own machine learning engineer, may be looking more for a background in software engineering. Whatever the case, you’ll probably need to tailor your resume a little to the needs of your business. Here are some skills and attributes that I can highlight for different categories of computer science:
- Data Analyst / Engineer: SQL, Pandas, Feature Design, Spark (PySpark), Hadoop, NoSQL Databases
- Data scientist: Applied Statistics, Predictive Modeling, Pandas, Scikit-Learn, PyTorch, Tensorflow, Jupyter
- Machine learning engineer: CI / CD (eg GitLab, Jenkins), FastAPI / Flask, Docker, Kubernetes, AWS, Terraform
Most computer science has a multi-step process in hiring potential applicants. These steps usually involve some sort of coding, one or more telephone interviews, and usually culminate in a “personal” interview. (“Personal” in quotation marks, as most of the last interviews have now become virtual, especially after COVID-19.) If a candidate enters the final round of interviews, he or she usually has the same qualifications as the other candidates who also made it to the final round. In other words, an employer usually cannot go wrong by hiring candidates who make it to the final round.
Here, irrationality returns to the game. If two people are equally qualified, how can you decide who you want to hire? If you’ve ever heard of the “airport rule,” that rule basically states this: if you have to hang out at the airport with someone for a long time, who would you like to spend that time with? That’s why I think personality sharing is important, because in this round of interviews, this can really be a differentiating factor that gets you to work. Again, it doesn’t quite make sense, but it’s the way the world works. And I’m less frustrated with this, as I was from the headshot, because personality can show how well a person works with a team, which is really an important factor.