Issue #37: Reflecting on job searching again

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📰 TL;DR

It turns out that targeting the specific responsibilities listed in job postings is a good way to get companies to notice you.

In this issue…

I wanted to reflect back on Issue 34, where I talked about my approach to job applications.

Issue 34: My job application strat

In short, the strategy is to make a conscious efforts to match as many requirements/qualifications on job posting as you can. If you do not have a experience for a bullet point, figure out a way to acquire it.

First, some context

The reason I want to do this is because — as I am writing this — I’m wrapping up my attendance to ENAR 2025, a mainstay conference for statisticians. Several companies are known to look for job candidates here, and I have been having terrible luck with just simple applications so far. I think meeting the companies at a conference is probably a faster way to get noticed since they have to see your face.

At my first conference, I thought you could walk up to companies and ask to interview. I learned that this was definitely not the case. Instead, companies will contact you before the conference and schedule an interview. If they don’t do that, then it’s almost impossible to convince them at the conference to put you in because their schedule is already filled.

Thankfully, I was able to convince a few companies to want to talk to me at this conference. During my interviews, I realized I didn’t include an important detail in Issue 34, and it’s this:

Filling in gaps in your skills will work, but it’s not worth it to rush these skills in. You can technically include a skill on a CV if you’ve learned it, but when you get to the interview table, you won’t have much to talk about.

My strategy performs best in the long game. Ideally, you should start looking at future job positions when you’re just starting your graduate education. I know that seems crazy if you’re just starting, but it is much better than blindly trusting that you’ll randomly run into relevant experiences. Your research/dissertation advisor is not in charge of giving you relevant experiences, you have to seek them yourself. You may be lucky and have an involved advisor who actively facilitates your career growth; in observing other Ph.D students, this is usually not the case.

One thing I wish I would have started long in the past is to keep a list of skills, responsibilities, or qualifications I want to have. Something like:

1. Communicate statistical ideas with non-technical stakeholders both in written and verbal forms

2. Write statistical analysis plans

3. Create randomization lists, do sample size calculations and power analyses

4. Be able to conduct standard analyses in R or SAS

After making this list, you set a recurring reminder to go back to this list and then you reflect if you’ve done anything to make progress on any of these. Sometimes, things will be slow. Other times, you’ll have found you’ve grown a lot. I’ve found that a lot of growing happens after stressful periods of learning or deadlines.

This approach is preferable to the approach that I’ve been using for most of my life: updating my CV/resume only when it’s time to apply for jobs.

Inevitably, you will forget useful experiences or stories that could act as stories or answers to potential questions about my skills. For this conference, I remembered some major projects that I had forgotten about because they were finished 2-3 years ago, and I had moved on. One of these stories was pivotal in convincing a company to follow up with a second interview, and I almost didn’t have it in my brain!

This advice is not just for students in graduate school. I think it could be applicable to anyone looking for a job. It’s really easy to forget to log your experiences when you’re focusing on work. I’m thankful to my past self for looking at job postings when I was procrastinating on studying for the qual.

Aside: some interview tips for statistician positions

Before I wrap this up, here are some advice I gleaned from listening to my interviewers:

1. Everyone applying to statistician positions will have an MS or PhD. Everyone is technically qualified, so often the main distinguishing factor between how well students can communicate about statistical ideas on varying levels

2. Students working in technical fields often believe they can stay purely in a research/technical world in industry. This is a mistake. It’s possible to stay working on technical stuff, but applied work cannot be ignored

3. Similar to point 1, you should know how to talk about any detail about your project, especially on how it solves more general needs (read: the company’s needs). Student applicants often get stuck humblebragging about how technical a proof or method is, without explaining how it will benefit someone else.

4. Practice your talking points with actual talking. It is common to write out talking points, but people talk differently than how they write. Coming off as if you’re memorizing an internal script via unnatural phrasing isn’t a favorable look.

That’s it for this one, see you in the next one.

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Footnotes

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