Job Search

Changes for 2024: Resume revamp v1

Goal: To review my current resume/LinkedIn for keyword/skill matching and rewrite/edit as needed to better match job ad language.

To prioritize a first pass, I went back to my google sheet that sorted keywords by the number of categories they were associated with and highlighted everything with 4+ categories. Then I went into my Airtable (keywords sorted by total jobs associated with them) and copied everything with 4+ jobs, pasted it into the same column of the spreadsheet with the label “FREQ” in the next column, and created a conditional formatting rule to highlight duplicates. There are 36 keywords that overlap, though both lists are worth reviewing.

Did a quick pass of the resulting list to cross out skills/experiences I don’t have (commercial clients, cybersecurity) and italicize the weak/personal ones (accessibility, Figma). These are areas I’d love to get more training and experience in, but that’s not the point of this exercise.

Working with a text-only copy of my existing resume, I went through the list and made a list of keywords I could readily associate with each job/educational experience.

I also added keywords under my grad degree, because it’s some of my stronger recent experience and covers a lot of skills not found elsewhere on my resume/portfolio. I highlighted a few terms that are worth calling out for that reason specifically.

There are a few roles I’ve historically left off the resume I send out due to space concerns and lack of relevance, but for the sake of completeness, I reviewed them for keyword relevance as well. At the very least, I can update them on LinkedIn.

For my first pass, I started with the shortest job descriptions and began drafting new descriptions, referring to the keyword list as much as possible. Once I was satisfied with the updated descriptions, I made a fresh formatted resume, taking into account a few things I’d read about applicant tracking system-friendly design and keeping it very simple and consistent. I even left off my cute little logo. Although it’s my goal with this process to create a modular resume that can be longer than one page if necessary, the first revision is meant to replace my basic one-page resume.

Once I felt good about my new one-pager, I tested it with one of those sites that scans resume text and compares it to a given job description, offering feedback on keywords and text parsing. My experience with that site was that it really wanted to sell me something, and hey, I’m unemployed, I’m scrounging my pennies, gimme a break, but thank you for the feedback that wasn’t behind a paywall, anyway. I made a few tweaks and downloaded my one-pager as a PDF, ready to submit to the next opportunity.

I also updated LinkedIn with these edits and added the update as a “Meeting” in my application tracker Airtable, just for my own records. Additionally, I did a quick pass cleaning up the tags associated with my portfolio site, since they’re displayed in a cloud for navigation, as well as adding these blog posts at the bottom of the portfolio page (oh hey!).

For my next trick, I’ll be going category by category, with a similar process as the starting point, albeit with different filters on frequency. I may also attempt to roll up some of the “tail” keywords (those associated with one role in my sample) for patterns that can contain multiple keywords or easily be edited to match a job description’s language.

Job Search

Changes for 2024: Keyword review process

In order to make informed choices when revamping my resume to better target jobs in my top categories, I needed a list of keywords found in actual job ads. It would be time-consuming and likely futile to revamp my resume on a precise keyword-matching every single time I send it out, so I set out to build a little keyword database.

In order to collect keyword data, I spent a week pulling job ads from my usual assortment of sources—LinkedIn, Indeed, Otta, specific regional and career-interest job boards and mailing lists—including some that, while they fit into the categories I typically targeted, were not necessarily positions I would apply for, due to skill/interest mismatch or failure to meet some other personal criteria like salary. I copied the text of the job ad into the notes field of a new Applications record and set as Status = Considering. The purpose at that time was not to evaluate whether to apply, but to perform a short manual content analysis of the ad itself. (I did go back and review the list, applying for several things while moving others to Pass.)

While there are undoubtedly a variety of tools that could attempt to perform a keyword extraction for me, I don’t find that I’m content with relying on them, at least not in this case. I needed to read the ad for context, to attempt to understand the way the hiring team describes the organization, the role, who the person in the role will work with, what the role will entail, and the qualities and experiences expected of such a person. With the job description and the record entry screen up side-by-side on my monitor, I could read the ad and type keywords as they came up. As I reviewed more ads, I developed an ad hoc taxonomy of sorts, sometimes opting to roll together similar-enough concepts instead of creating a new linked keyword record (ex. “building relationships” and “relationship building”; merging “integrity” and “accountability”), occasionally editing the keyword record itself to better reflect what I saw in the job ads. 

It’s far from a perfect system—but it doesn’t need to be. I just needed to get a list of keywords, ideally filterable by category, to identify how to focus my resume edits.

From January 8–16, I thoroughly reviewed 43 job ads from seven different sites. Nearly half (22) came from my LinkedIn alerts and recommendations. From those, I created 263 unique keywords. The top three keywords were “communication” (25 jobs), “cross-functional collaboration” (17 jobs), and “stakeholder management/collaboration” (16). 117 keywords were applied to only one role. As part of my next steps, I will review these for additional patterns to inform resume edits.

It’s clear that many top keywords are shared across multiple categories. These are words/phrases and concepts I will ensure are reflected in the next version of my resume (to the extent that they apply to my experience). But in order to create specialized (or modular/tweakable) resume versions to better target certain areas, I need to break down keywords by category and look for similarities and differences. Unfortunately, Airtable doesn’t seem to make this easy, at least not without paid features (there are other ways to figure this out without spending money).

Starting from a Roles view that grouped records by category, I copied the lists of keywords into a spreadsheet in order to clean up and organize the keywords two different ways: 1) keywords associated with more than one category, and 2) unique keywords by category. The dataset was not so large that I couldn’t accomplish this somewhat manually, using some combination of text to columns, deduplication, and sort functions along with paste transposed.

There were 123 keywords associated with more than one category, with a total of 42 unique category pairings/groups. Common pairings included Content and Design (52 keywords), Analytics and Content (49 keywords), and Design and Research (48 keywords). Other common groupings included Content/Design/Research (34 keywords) and Analytics/Design/Research (30 keywords). I’ll use this information in conjunction with overall frequency to prioritize words and phrases to ensure they appear in the next version of my resume.

For keywords by category, I copied lists from four well-represented categories to sort and deduplicate: Content (120 unique keywords), Research (101), Analytics (105), and Design (98). Here I’m not looking for similarities across categories or frequency of keyword appearance. I just want a list of keywords associated with jobs according to category. This will help me identify opportunities to create specialized resumes for these categories and possibly modular components targeting specific qualifications and experiences that will make it easier to generate cover letters and/or specialized resumes. Not every keyword is one I can use—I don’t have all those skills in my toolkit, though there are some I’d love to learn—but it’s a great starting point.

That said, I’m a little intimidated by the work ahead! I’ll do my best to document my progress.

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2023 in Review: Role categorization & success rate

Over the course of 2023, I submitted applications for 189 job opportunities. For my troubles, I had two interviews and zero offers.

It’d be easy to chalk it up to a rough job market or despair my spotty resume (between grad school, the pandemic, and generally trying to manifest a career shift, the last several years do not look great!), but neither of those things helps me actually get a job. So I set myself the task of evaluating my applications based on the (little) information I had.

My first step was breaking down the data by Role. Of those 189 applications, there were 130(!) unique job titles, but many of them were similar roles, so I created an ad hoc classification system.

On the first pass, I started with labels that more or less matched the original job title—“Content Designer” and “Senior Content Designer” under Content Design; “UX/UI Designer” and “User Experience Designer” under UX Design, and so on. There were a few roles that didn’t quite fit easy boxes—Other Analytics and Other Content were a bit of a catch-all, but it was all right for a first pass. 

Then I wanted to see if there were any conclusions I could draw about my applications and response patterns within those categories. Since my overall success rate (let’s say getting to the interview stage is a “success”) was negligible, I thought maybe I could see something in my rejection rate. I copied the grouped data into a spreadsheet just to make it a little easier on myself and calculated the percentage of explicit rejection responses for each role category to compare to the overall rejection rate. On doing so, I realized that most of the role categories represented too few applications to offer much insight, so I decided to group the categories into broader labels: Content, Information, Research, Analytics, Design, Writing, and Management.

Now, I don’t have any solid takeaways about explicit vs. assumed rejection across categories. Perhaps the hiring teams in some categories are more responsive than others. But there was a pattern, and that’s something to go on for my next steps.

With an overall explicit rejection rate of 40%, two categories were lower, two were higher, and one was about the same. (NB: these calculations reflect an earlier iteration of the “super category” breakdown.)

CategoryExplicit Rejection Rate
Content/Information (combined)32% of 79 applications
Research/Analysis (combined)60% of 47 applications
Design39% of 33 applications
Project Management47% of 15 applications
Support22% of 9 applications

The higher explicit rejection categories suggest a need to better tailor my application materials for those roles, while an overall enhancement in matching my experience to job descriptions is clearly called for. I’ll go over how I’m approaching that process in another post.

Finally, someone reading this might be wondering: Emily, why are you applying for jobs in so many areas?! Just pick one thing and target it with gusto! Look. I’ve tried, OK? I am a square peg in a world of round holes. I’ve got a bunch of interesting, useful experiences, a semi-fancy graduate degree, and a whole lot of passion and willingness to just figure stuff out, but it doesn’t fit any one little box all that well. Am I a designer? Well, sure, in some respects. Researcher? I think I fit the requirements in some places; less so in others. Content strategist? With some very particular experiences, yeah! There’s nothing universal about the way these positions are described or advertised. I’m just a person with a pretty good brain I’d like to put to good use—in exchange for money so I can live my life. So I’ll probably continue to cast a wider net… just maybe with somewhat more specialized fishing equipment, so to speak.

Job Search

Using Airtable to track job applications

I’ve been actively looking for a full-time job since early 2023. But I’ve been in the market since long before that. It hasn’t been easy. At all. But in that time, I have been trying a lot of things to keep track of the effort, and I really want to talk about it.

I’ve always been a bit of a spreadsheet fanatic. What project isn’t made better with even a quickly slapped-together spreadsheet? But while attending a conference  in grad school, I learned about Airtable and instantly wanted to give it a try. I used it for a class project or two, my e-portfolio for graduation, and even planning an elaborate holiday meal (complete with shopping lists, prep timings, and recipes). So naturally, when the time came to re-enter the job market earnestly, I made an Airtable to track my applications.

To be completely frank, I’m not certain anymore what it looked like at the beginning, because I routinely iterate and improve the process, but here’s a snapshot of the primary data view (job applications) by the end of 2023:

The primary purpose is, of course, to track the status of individual job applications. I chose Company Name as the data key value, even though there are some duplicates—there’s less overlap than job titles, and this is a bit easier to scan and sort. Role was originally a single-select, but became a linked data item—more on that later. Other immediately visible metadata for each application includes Link, Notes, Location (predominantly Remote), Action Date, Applied checkbox, Via (source), and Status

In the screenshot above, Status is used to group the view in order to filter out or collapse applications I’m not actively tracking. I developed a discrete list of status options, numbered to make data entry and sorting/filtering easier. 

  • 1a. Considering: For roles I’m entering into the sheet without immediately applying.
  • 1b. Pass: Roles that I entered but did not apply for. Might be tracking something else, or I hit a sticking point in the application process and stopped.
  • 2. Awaiting response: Application sent!
  • 3. Open process: Direct correspondence with the hiring team, interview scheduled and/or completed, additional application evaluation tasks, etc. Activities tracked under Meetings table if scheduled or under notes if not.
  • 4a. Offer: Self-explanatory.
  • 4b. Rejection: Explicit “no” from the hiring team in some form.
  • 4c. Assumed rejection: At the end of each month, I switch everything that’s 2+ months old from “Open process” to “Assumed rejection” so as not to clutter my tracking sheet. Easily updated if the status changes with direct correspondence.
  • 4d. Role canceled: Received a message from the hiring team that they were no longer hiring for the role despite failing to hire anyone. Added this later to track separately from definite nos.
  • 5a. Hired: Self-explanatory.
  • 5b. Resigned: Had to add this after my first post-grad school role turned out to be a poor fit.
  • 6. Absolutely not: Every once in a while I see a job ad that is full of red flags or things that I absolutely would not do for personal or ethical reasons. I track at least some of these to remind myself that I’m actively making choices in my job search, even if it doesn’t always feel like I have many choices to make.

Most of the time, I enter roles at “Open process” and they move directly to “Rejection” or “Assumed rejection.”

The Active status view is where I primarily enter and track applications. It only shows some statuses and is filtered by action date. All positions is not filtered at all and sorted alphabetically by company, which is useful when sifting through job ads to see if I’ve already applied to something at that company and made relevant notes or found it via another site. (Roles isn’t one I actively use, but it’s useful for filtering linked data. Maybe. Jury’s out.)The first linked data I set up was Via (Source). This is pretty straightforward: there’s a table of job listing sources I maintain—the obvious sources like LinkedIn and Indeed, of course, but also various organization-specific job boards, curated mailing lists, and communities are listed, as well as “Personal contact” for opportunities referred to me. Some of the sites I go check manually, so the table serves as a bookmark list. It’s also handy for tracking how generally “useful” various larger platforms are as far as leads.

Meetings are also linked data. There’s a calendar view under that tab, and a space to keep notes for each interview as well as upload any attachments they might send. I also track the event type, including one for “Content.” I added that when I wanted to track dates around things like profile changes and website updates.

Observant folks may have noticed that Role is linked data as well, but that was a more recent development. I’ll describe the reason and the process in another post.

The Notes field is handy for capturing anything that might be relevant. Sometimes I’ll paste the entire job ad in there. Sometimes I’ll include copies of questions and answers from the application form so I have a record of my written responses. Sometimes I’ll just complain about something annoying in the application itself, or make a note about some missing or confusing detail. At any rate, it’s freeform text content—formattable, at that—that is a handy reference.

P.S. Get in touch if you would like a link to the Airtable base! I’ve been making it up as I go, but I’m happy to share.