Ian Klosowicz
Apply if you meet 60 to 70 percent of the listed requirements. That's the threshold. Below that, you're wasting your time and the company's. Above it, the job description is probably describing an ideal candidate that doesn't exist, and you have a real shot.
Job descriptions for data analyst roles are notorious for this. A company that would happily hire someone with SQL and Excel experience writes a posting that also lists Python, R, Tableau, Power BI, machine learning familiarity, and 2 years of experience. None of those requirements are weighted equally, and the posting was probably written by someone who doesn't actually know what the job requires day to day.
Here's how to read a job description as a data analyst candidate, which requirements actually matter, and where to draw the line.
A job description is a wish list, not a contract. Companies write them to describe the ideal candidate, knowing they'll probably hire someone who hits most of the list but not all of it.
This is documented hiring behavior, not optimism. Studies on job application patterns show women tend to apply only when they meet close to 100 percent of listed requirements, while men apply at around 60 percent. The outcome isn't that men are more qualified; it's that they get more interviews. The lesson isn't about gender. It's about what the threshold actually is.
For entry-level data analyst roles specifically, 60 to 70 percent is the floor because these postings are frequently padded. A company hiring its first analyst often lists everything they've ever heard of. A company replacing an analyst who did everything lists that person's entire skill set, including the things the next hire will learn on the job. You're not expected to walk in knowing all of it.
Most job descriptions organize requirements into 2 buckets, even if they don't label them that way:
Hard requirements: things the company will actually screen for. These show up with language like "required," "must have," "minimum," or they're listed under a "Requirements" header. If the posting says "SQL required" and you don't know SQL, that's a meaningful gap. If it says "2 years of experience required" and you have none, that's the company signaling they'll pass on anyone without it.
Nice-to-haves: things that would help but won't disqualify you. These show up under headers like "Preferred," "Nice to have," "Bonus," or "A plus." A Python preference when the role is clearly Tableau-focused is a nice-to-have. Experience with a specific internal tool you've never heard of is almost always a nice-to-have.
When a posting doesn't separate these clearly, which is common, read the first 3 to 5 requirements listed. Those are the ones the hiring manager cares most about. Requirements 8 through 15 are usually padding or aspirational additions made by a committee.
Also read the job description itself, not just the requirements list. The day-to-day responsibilities section tells you what the actual job is. If SQL, Excel, and Tableau appear in the responsibilities and those are skills you have, you're qualified in the ways that matter even if the requirements list also mentions Python and you barely know it.
A short list of things that will genuinely get your application filtered out:
Everything else is negotiable. The 3 years of experience requirement for an entry-level role is not a real gating factor for most companies. The Python preference when the role is described as dashboard-heavy is not a real gating factor. Treat those requirements as context, not as hard stops.
Experience requirements on entry-level postings are the most widely gamed number in job descriptions. A posting that says "entry level" and then requires 1 to 2 years of experience is describing a market reality that doesn't actually hold in practice.
Here's why: companies write experience requirements based on what the last person in the role had, not what the role actually needs. If the previous analyst came in with 1 year of experience from an internship, the next posting says "1 year required." If a senior analyst left and the company is trying to fill the same role more cheaply, the posting might say "2 to 3 years" for what is functionally an entry-level position.
The ATS filter is the real risk. Some applicant tracking systems will auto-reject anyone who doesn't enter a minimum years of experience that matches the requirement. If the system asks you to self-report years of experience and you enter 0 for a role requiring 2, you might get filtered before a human sees your resume.
This is a genuine constraint, not a reason to lie. Don't falsify your experience. What you can do: count internships, part-time work, and significant independent project work toward a total. If you've spent 6 months building portfolio projects seriously and have 6 months of part-time or internship experience, 1 year is defensible. If the gap is larger, apply anyway and see what happens. Many ATS systems are set up poorly and don't apply experience filters consistently.
I've seen this pattern across thousands of people breaking into data analytics. The experience requirement is frequently the most intimidating part of a posting and one of the least reliably enforced. Apply and let the actual screen determine whether you're qualified, not the number on the posting.
The resume is where you control what a hiring manager sees first. When you're missing a listed requirement, the goal is to make sure your strengths are prominent enough that a recruiter reading your resume in 6 seconds is thinking about what you can do, not what's missing.
Practical moves:
If you want a resume template structured specifically for entry-level data analyst applications, including how to position transferable skills when your background isn't a direct match, Month 1 of the program at Analyst Hive covers this in detail.
Entry-level job searches run on volume. Response rates for cold applications are low for everyone, typically 5 to 15 percent, regardless of how well you match the posting. If you're only applying to roles where you meet 100 percent of the requirements, you're artificially shrinking your pool and extending your timeline.
At 10 to 15 applications per week with a 60 to 70 percent match threshold, you're probably applying to 3 to 4 roles a week that require something you don't fully have. If 10 percent of those convert to phone screens, that's still a conversation you wouldn't have had if you'd screened yourself out.
The cost of a rejected application is zero. Your time to apply is 20 to 30 minutes with a tailored resume. The upside is a phone screen that leads somewhere. The math consistently favors applying over waiting until you're a perfect fit.
There was a stretch in my own search where I was applying to roles that felt like a stretch on paper. The role that eventually worked out wasn't one where I checked every box. It was one where I checked the boxes that mattered to the people in the room, and they were willing to bet on me for the rest. That's how most entry-level hires actually work.
Building the targeting system that helps you identify which roles are genuine stretches versus realistic applies is part of Month 3 in the program -- join Analyst Hive on Skool.
What if the posting says "2 years required" and I have none?
Apply if you meet the core technical requirements. Experience minimums on entry-level postings are routinely ignored by hiring managers who find a strong candidate. The risk is ATS filtering, which you can't fully control. Apply anyway and see what happens. A rejection costs you nothing; a missed opportunity costs you the role.
Should I mention in my cover letter that I'm missing a requirement?
Only if the gap is significant and you have a genuine response to it. "I don't have Python experience, but I'm actively working through X and have built Y with it" is worth including if Python is listed as required. Flagging minor gaps or nice-to-haves you're missing is unnecessary and draws attention to the wrong things.
Is it worth applying to a role if I only have 50 percent of the requirements?
Usually not. At 50 percent, the gaps are large enough that you're likely to get screened out early, and even if you make it through, you'll be underprepared for the technical screens. Use that application slot for a role where your match rate is higher. The exception is a role at a specific company you really want to work for; a long-shot application there might be worth it even at lower match rates.
Do hiring managers actually read every resume?
No. Most entry-level applications get 6 to 10 seconds of initial review if they make it past the ATS. The first pass is a skim for job title match, tool names, and obvious red flags. A resume that passes that skim gets a longer read. This is why keyword matching and leading with your strongest qualifications matters more than covering every listed requirement.
What if I get an interview and they ask about a requirement I don't meet?
Answer honestly and pivot to what you do have. "I haven't used R professionally, but I've done similar analysis in Python and I'm comfortable picking up new tools quickly" is a reasonable response. Lying about a skill in an interview is a fast path to being caught in a technical screen or on the job. Honesty paired with genuine adjacent experience is a better position than most people think.
How do I know which requirements are the ones that actually matter?
Look at the job responsibilities section, not just the requirements list. The tools and skills mentioned in the day-to-day responsibilities are the ones the role actually uses. Requirements listed after the first 4 or 5 are increasingly aspirational. Cross-reference the requirements with similar postings from the same company or similar companies; the requirements that show up consistently across postings are the ones that matter.
