Saturday, January 18, 2020

How to be a good candidate for analytics and data science jobs

Over the last few years, I’ve done a lot of hiring across many disciplines: analytics, data science, product management, engineering, sales, and HR. I’ve learned a lot about what makes a good candidate and what makes a bad candidate. Because I’m writing this blog for people interested in analytics and data science, I'm going to share some of the things that I think are likely to improve your chances of getting hired for technical positions.

(Image credit: Grey Geezer at Wikimedia Commons - license. Image unchanged.)

Hiring is risky

The key thing to remember is hiring is a tremendously risky process for the employer. It’s very painful to unwind a poor hiring decision, so for the most part, the interview team is not inclined to take risks. You have to satisfy the technical requirements for the job, but also the social requirements too. The interview team will be deciding whether or not you’re a fit for the team - can they work with you? There are all kinds of clues they use to decide this and I’ll cover some of them here.

Resume blunders

Candidates make amazing blunders with resumes. I’ve seen odd layouts, poor wording, and incredibly long resumes (15+ pages in one case). Here are some simple rules:

  • Length: one page if you’re junior, two pages (at most) if you’re senior.
  • Layout: single-column layouts - keep it simple.
  • Keywords: your resume should use every relevant keyword as many times as it makes sense. For example, if you have machine learning experience, use the term. Resumes are often keyword screened and if you don’t have the keywords you’ll be ruled out by a machine.
  • Contact details: name, city, phone number, email. I always give local candidates preference, but I have to know you’re local.

Your resume gives clues to how well prepared you are (back to the risk thing), a bad resume indicates you haven’t taken advice, or you don’t care, or you’re naive - none of which are good. There are plenty of good resources out there for building resumes. Northeastern University does an incredible job preparing its candidates for work, including some great coaching on resume building. They have an excellent website on resumes with lots of strong guidance.

One great piece of advice I’ve heard is to customize your resume for the employer or industry you’re targeting. Some candidates are considering different employment areas but they have a single resume they’re trying to use for everything. You should have a different resume focusing on different areas for each industry you're targeting. If you have time, you should tweak your resume for each employer. Remember that customization is as much about what you leave out as what you leave in. For example, if you have wet bench experience but you’re applying for computing positions, you should shorten (or remove) your wet bench sections and increase the length of your software sections. The logic here is simple, you have limited space, so why tell an employer about something irrelevant to them? For me, there’s a minor exception - I do like candidates with something unusual about them, but a single resume line is usually enough (e.g. ‘wet bench qualifications’, ‘EMT qualified’).

Github!

I love it when candidates have a Github page they put on their resume. If they pass the screening interview, I check out their page and what they’ve done. You do need to be careful though, I’ve seen some bad code that’s put me off a candidate. Github is especially great if you’re trying to do some kind of career transition into analytics or data science from some other field. If you’re transitioning, you can’t talk about what you’ve done in your current role as proof of your capabilities, but you can talk about the Github projects you’ve created in your own time. In fact, creating a project in your own time to display your work shows a tremendous amount of commitment. If you have projects to put up on Github, do so, it’s a great place to demonstrate your talent.

Be prepared - and turn your camera on

If you haven’t interviewed in a while, it’s a good idea to reach out to your connections and ask for a practice interview. You could also ask your friends for a review of your resume. Of course, you should remember that if people help you, in turn, you should help people.

For heaven’s sake, be technically prepared for the interview. Nowadays, many interviews are conducted via a computer video call (e.g. Skype, Zoom, etc.). There’s almost always software to download and install. Make sure you have the software installed and running before the call.  I interviewed someone for a management position who took 20 minutes to download the software and get into the interview. Not good when you’re interviewing for a position that requires experience and forward planning!

For video interviews, I have two pieces of advice: turn your camera on and consider where you do the interview. It’s a video interview for a reason and it looks odd if you don’t turn your camera on. I was once told that the reason why a candidate didn’t turn their camera on was that they’d had an unusual hair treatment just before the call. Your hair is your business, but why not schedule the call for some other time? You also need to consider your background; what will the interviewer see? I was once interviewed by someone from a hotel bedroom with their underwear strewn everywhere in the frame - it didn’t create a professional impression. One candidate I interviewed had their laptop on their knees for the interview; every time they moved the entire video frame heaved like a ship in a storm and by the end of the interview, I felt seasick. Try to avoid distracting locations and distracting items in the frame.

Preparation also means understanding who will interview you and what the interview will cover. I’ve interviewed candidates who were surprised to be asked technical questions when the interview briefing clearly said that would happen. Of course, you must look up everyone on LinkedIn beforehand and know their roles - you might even get insight into the questions they might ask.

Examples and questions

A few years ago I did a course on behavior-based interviewing. There were lots of great pieces in the course but it can be boiled down to one simple idea: give examples for everything you claim. For example, if you claim to be a good planner, give examples of how you planned well, if you claim to know Python, point to examples (e.g. Github), and so on. The idea is you’re providing proof - doing is better than saying.

Make sure you have plenty of questions for each interviewer. It shows you’re prepared and engaged, and of course, you might learn something useful. It’s also expected. If you can, get every interviewer’s email address, you’ll need it later.

At the end

When it’s all over, send a thank you email to everyone who interviewed you. For any kind of customer-facing role, this is expected and it’s increasingly expected for technical roles too.

If you don’t get the job, there’s one last thing you can do. If you got on well with the interview team, ask for feedback Not every interview team will do it, but some will and you can learn a lot from them about why you didn’t get the job.

Final thoughts

Bear in mind that a lot of what I’ve said is about reducing risk for the employer in choosing you. Being prepared for the interview (software download, video call background, interview questions, etc.) shows you take it all seriously and gives clues to what you’ll be like as an employee. Asking questions at the interview and thanking everyone shows you know about social conventions and could be a good fit for the team.

Good luck!

No comments:

Post a Comment