And as people congregate for the holidays, cases are likely to rise in the West all over again. In America, they are set

Is there something you’ve worked on that you can share on GitHub? Any Kaggle competition or side-project can be very helpful, and enables looking at concise code, types of preprocessing, feature engineering, EDA, choice of algorithm and countless other issues that need to be addressed in a real-life project. Add a link to your GitHub and Kaggle account for interviewers to dive into your code. If you don’t have much experience, there’s a good chance you’ll be asked about one or more of these projects. In some interviews I had, the candidate didn’t remember much about the project and we couldn’t develop a conversation regarding the choices they made and the reason behind them. Be sure you brush up on the work you did or keep it out of the CV. Similarly, make sure you present your best work and you’ve put enough time and effort into it. It’s better to have 2–3 high-quality projects than 8–10 medium (or lower) quality.

This kind of person is acting thoughtlessly, with a kind of massively idiotic indifference — shopping and eating and partying like it’s a normal holiday season — because they can. They can act thoughtlessly precisely because nobody much is examining the big mistake the West made, which let Covid get so bad, spiral out of control, make it something that’s simply dragging on and on and on.

Type of algorithms — structured/classic ML vs Deep Learning. Some candidates have only worked with Deep Learning, including on structured data that could have been better suited with tree-based models. While there’s no problem per se with being an expert at DL, limiting your toolset can limit your solution. As Maslow said: “If the only tool you have is a hammer, you tend to see every problem as a nail.” At Riskified we deal with structured, domain-driven, feature-engineered data which is best dealt with various forms of boosting trees. Having someone whose entire CV points back to DL is an issue.

This can generally be broken down into languages, specific packages (scikit learn, pandas, dplyr, etc), clouds and their services (AWS, Azure, GCP) or other tools. Some candidates mix this up with algorithms or architectures they are familiar with (RNN, XGBoost, K-NN). On a personal note, I prefer that this revolve around technologies and tools; when a specific algorithm is mentioned it makes me wonder whether the candidate’s theoretical ML knowledge is limited to just those specific algorithms. Here, I’m looking for the relevance of the tech stack — are they from the last few years (a positive sign that the candidate is hands-on and learning new skills), the breadth of the stack (are they very limited to specific tools or are they familiar with quite a few things) and the fit with our stack (how much will we need to teach them).

I’ve also seen this approach abused even further by taking the subjective measures and turning them into a pie chart (30% python, 10% team-player, etc). While this was probably supposed to be a creative way to stand out, it demonstrates a lack of basic understanding behind the concepts of different charts.

I’ve seen some beautiful CVs (I’ve saved a few of these for personal inspiration) but I’ve also received text files (.txt) that lack any formatting. Working on your CV can be a pain, and if you’ve chosen data science as your endeavor there’s a good chance you don’t enjoy creating aesthetic designs in your spare time. Without going overboard, you do want to look for a nice template that enables you to get everything across in limited space. Use the space wisely — it’s useful to split the page and highlight specific sections that don’t fall under the chronological work/education experience. This can include the tech stack you’re familiar with, a list of self-projects, links to your github or blog and others. A few simple icons can also help with emphasizing section headers. Many candidates use 1–5 stars or bar charts next to each language/tool they are familiar with. Personally, I’m not a big fan of this approach for several reasons:

Visually appealing CVs of Data Scientists, details blurred. Note the vertical split used in both examples to differentiate experience, skills, achievements and publications. In both cases, a short summary paragraph helps describe their background and desires. Used with permission from owners.

Some of these people — many of them, maybe — are Covidiots, skeptics, nutcases, radicalized by even bigger idiots, thanks to Facebook and Twitter and so on. They’ll actively deny Covid exists or will minimize it as being just like the flu. But many more of them are another kind of idiot. They know Covid is real, and they even know that it’s dangerous. But they’re not able to think about it well, because nobody has taught them to. So they shrug and try to desperately make life resume as normal. Who’s the more dangerous idiot, then — the Covidiot, the denier, the malicious one, or this kind of person, the one who’s an idiot by way of indifference, carelessness, thoughtlessness?

I live, part of the year, in one of the West’s most lively neighborhoods. A place famed for its bars, restaurants, clubs. In a regular year, millions of visitors pack it full, thronging the avenues for a taste of the culture, the nightlife, the music, and art that it gave birth to. For much of this one, though, my neighborhood was empty. Until recently, that is. Today, we went out for a little walk — and there everyone was. It’s Christmastime all over again — and the shoppers packed the stores, the pedestrians lined the avenue, the families strolled down the streets.

I tell you this story for a reason. It’s not (just) to shame all these people, though perhaps they do deserve a little bit of shame. What are they thinking, pretending like things are normal, while a deadly virus rises to new heights? The answer, though, is that they’re just thinking what they’ve been told to think, which is…nothing much at all. Let me explain what I mean.

It’s so simple it hides in plain sight. The West didn’t try to stamp out Covid. To reduce cases to zero. The attitude in the West was something like: “We’ll do what we can to minimize and lessen this pandemic.” It wasn’t: “we’re going to eradicate this deadly disease, and wipe it out.”

ML Domain — this is usually relevant in two domains that require much expertise — computer vision