225: Why This Data Analyst Got 0 Interviews (According to a Recruiter)

Speaker: 00:00:00

So this data analyst has

gotten zero interviews in eight

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months of applying for jobs.

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So we're gonna talk about why this

is the case and how they can fix

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it and how you can fix it if you're

struggling to land interviews

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If you are struggling to land

interviews, it's not you.

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You're not the problem.

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It's likely something that's

either your LinkedIn or your resume

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that's not optimized, that is not

actually getting you in front of

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hiring managers and recruiters.

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It's not getting you past the

applicant tracking system,

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and it's keeping you stuck.

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You might think that you suck.

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You might think that your skills suck.

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You might think that you're not cut

out for data analytics, but you are.

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You just need a good resume.

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And today, we're gonna be

looking at a not so great resume

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and how we can make it better

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one of the easiest ways to make

your resume better is just to

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start with a really good template

with built-in good structure.

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So I actually have a free template

for you to download and just use, and

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just trust me, I have been helping

people land data jobs for five years.

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This resume really works.

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You can go to

datacareerjumpshot.com/resume

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or find the link in the show notes

down below to get that resume.

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this resume is going

to do wonders for you.

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It's gonna save you a lot

of time, and it's 100% free.

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So go grab it right now

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We'll be reacting to a video that was

done by a gentleman named Headless

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Headhunter, that is a mouthful, uh,

from a data analyst resume that he got

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submitted to him on his YouTube channel.

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I'll have a link to his channel in the

description down below, and if you're

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listening on the audio podcast, I'm

gonna try to do my best to describe what

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this resume looks like and what we are

looking at throughout the entire process

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Avery Smith-2: All right, here we go

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Avery Smith's screen-2: Recruiter

here to review your resumes.

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The resume we have up first is a

data analyst, and this person has

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been applying for eight months

and has gotten zero interviews.

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So this is a real stinker of a resume,

and I'm here to tell you why it's bad,

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how to fix it so that you can get a job

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Avery Smith-2: Just one note, just

because, you know, they haven't been

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able to land a job in eight months

doesn't exactly mean the resume is bad.

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That's probably one of

the things it could be.

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But if you only applied for eight

jobs in eight months, you're also

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not likely to get any interviews.

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So it also depends on, you know, how

many applications you've sent out.

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If you sent out, you know, hundreds of

applications and you have no interviews,

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then yes, definitely a problem.

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But just remember that, like,

it's not just your resume,

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uh, that gets you interviews

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Avery Smith's screen-2: Uh, that is

my job as the headless headhunter.

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So what do we need as a data analyst?

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We need degree, industry, SQL

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Avery Smith-2: Okay, so he's going

over the qualifications that a

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data analyst needs, needs, and

he's saying degree in industry.

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I don't know what industry means.

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Uh, degree, yeah, you can argue like

having a degree is helpful, but I don't

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know if he means a data analytics degree

'cause there's not very many of those.

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I don't have one of those.

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Um, so I'm not sure what he means there.

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But yes, there are m- there's not a

ton of data jobs that you can land

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if you don't have a college degree.

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It's possible, but it's a lot more work

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Avery Smith's screen-2: Data

visualization, Tableau, Power BI, Looker.

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Influence stakeholders.

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Explain technical concepts

to non-technical people and

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non-technical stakeholders.

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You absolutely need that.

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Your job is to convince Bob, who

cannot turn on his monitor, why you

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have SQL in what you do with the data.

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That is your job, and you need

to show me that in the resume

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Avery Smith-2: Uh, very important here

that like, yes, being a clear communicator

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is an important job as a data analyst.

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Um, and you need to be able to explain,

you know, complex things, numbers, uh,

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to non-technical people in a simple way.

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Um, I know he's just going this off

the cuff where he's like, "You need to

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explain to Bob why we're using SQL."

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And to be honest, most of the time you'll

be working at a larger company that

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already decided they're using SQL, and

it's not really your job as like a junior

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or intermediate data analyst to be like,

"We should switch to something else."

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And to be honest, what

are you gonna switch to?

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Like everyone uses SQL.

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So didn't love his example here.

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I'm being nitpicky, but I just

wanna, you know, bring up a point

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here that recruiters, they have to

know everyone's job inside and out,

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and that's absolutely impossible.

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So like just know that recruiters kinda

know what they're talking about, but

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not exactly, because he has to know all

the details of a data analyst, of an

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accountant, of a financial professional.

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Uh, you know, maybe he

does nursing, I don't know.

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Like rec-recruiters work for so many

different roles that they have to know,

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you know, these different role types

and they often don't a hundred percent

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Avery Smith's screen-2: I wanna

see how you solved a problem

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with data, not what the data is.

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This is another big one that data

analysts get wrong all the time.

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Also, I do more than tech resumes, such

as like software engineers, data analysts.

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I do like accountants and everything

else, but the tech market is so incredibly

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terrible that like 50% of the resumes sent

to me are tech, but I do do more than tech

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Avery Smith-2: So once again, he's

just proving the point here that

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recruiters, they often do lots of

different types of roles, and it's

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impossible for them to know, you

know, the ins and outs of every role.

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So when you're, when you're-- when

recruiters are reviewing your resume,

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they don't know everything, and so you

have to work really hard to try to make

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it as easy as possible for them to know

what you're talking about, and we'll,

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we'll get to this here in a second

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Avery Smith's screen-2: But a common

data analyst problem is they always

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tell me how they got the data when

nobody cares how you got the data

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Avery Smith-2: I just

don't think that's true.

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Like, how many of you guys…

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Let me know in the Spotify

comments and the YouTube comments

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that, like, are you putting how

you got the data on your resume?

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I think a lot of data analysts don't

talk about how they got the data.

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I think they talk about

how they analyze the data.

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A lot of the times, data

analysts don't get data.

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Uh, like it's already in a database,

so why would they, you know, list

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that as one of their responsibilities

or one of their bullet points?

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And if you did get the data on your

own, I think that's really impressive

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because getting data is really difficult.

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Like, if you web scraped data from

the internet, you know, that's hard

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to do, and that deserves a bullet

point, and that's useful for a lot of

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different industries, a lot of different

companies, a lot of different jobs.

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Um, so I don't really get why he's

saying, like, "Don't talk about, you

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know, where you got the data from."

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I don't think we are.

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And two, if you did get the

data in a unique way, I think

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that's worth pointing out.

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So I don't, I don't get his point here

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Avery Smith's screen-2: No one cares

if you used a Monte Carlo simulation

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to flip-flop the floop flop and

the blah, blah, blah, blah, blah.

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All they-

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Avery Smith-2: I, I think if you listen

carefully, he's like, "No one cares

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if you did a Monte Carlo simulation,"

which is one way to generate data.

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Like, if you don't have actual

data, you can simulate data, you

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know, and run, you know, hundreds,

thousands of different simulations.

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Monte Carlo is one, one way to do it.

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Um, but where he's just like flip-flop,

blah, blah, blah, blah, blah.

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I think that's what recruiters read when

they see a data analyst resume, is they

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see like one word they know, like Monte

Carlo simulation, and then it's just like

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blah, blah, blah, blah, blah, and it's

just like a bunch of jargon for them.

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So you just have to keep that in mind,

that recruiters don't know all of

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data analytics jargon that's going on

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Avery Smith's screen-2: They care about is

what you did with it, not how you got it

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Avery Smith-2: I do think this point is

really important, that what you do with

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the data is the most important thing.

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It is more important

than, than how you got it.

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Um, and we're not just analyzing

data for analyzing data's sake.

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We're not doing it for funsies.

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We're doing it for a purpose.

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And so it's really important to try to

illustrate why you did what you did.

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Like, how are you helping the

business in the big picture?

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Avery Smith's screen-2: I need to

see how you solved a problem with

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the data, not what the data is.

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I need to see MS Excel, yes, really,

macros, pivot tables, VLOOKUP,

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Word, Python, R, multiple projects

and deadlines, and nice to have is

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cloud AI and security clearances

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Avery Smith-2: So, uh, he

kind of went through the

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qualifications a little bit more.

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He said Microsoft Excel, macros,

pivot tables, and VLOOKUP.

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So I think this is really funny because

number one, I don't really think a lot

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of people are using macros anymore.

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They've always kind of sucked.

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Um, they take a lot of effort to code,

and they're very slow and not very robust.

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Uh, I think Python in Excel has

really taken over most macros.

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Uh, pivot tables are still the king.

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We still use a lot of pivot tables.

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Now, notice VLOOKUP here, and all of

you guys probably who are listening

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and, you know, have touched, you

know, Excel, you're like, "Oh, I

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know VLOOKUP, but XLOOKUP's way

better or INDEX MATCH is way better."

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And yes, that might be true, but my

point here is, remember recruiters,

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they don't know the difference between

VLOOKUP and XLOOKUP like you do.

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And if you're unfamiliar with it, it's

basically the exact same thing in Excel

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except for XLOOKUP's a lot easier.

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VLOOKUP, you have to like be a little

bit more specific with what, what data

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you're actually trying to look up.

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It's basically a way to search a

large data set, and if you know key--

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one key value, you can get its pair.

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Um, but my point here is like they're

looking for VLO-- the recruiter's

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looking for VLOOKUP on your resume.

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Um, and maybe even an applicant

tracking system, ATS is, is as well.

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So even though you might not use

VLOOKUP and you know XLOOKUP is

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better, it might be worth having

things like VLOOKUP on your resume.

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Um, also, I don't know when he's

saying, uh, bullet point of Python/R,

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multiple projects and deadlines.

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I think deadlines is interesting.

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I don't know.

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He didn't really expand on that.

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I think that's interesting.

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Nice to have Cloud.

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Yeah, it's nice to have.

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It's not listed in very many,

uh, data job descriptions.

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AI, this is in twelve percent now.

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Uh, security clearance, obviously,

that's not something you can

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really just go out there and get.

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So, um, those are some nice to haves.

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Avery Smith's screen-2: So

I only have twenty seconds

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to find what I need to find.

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If I cannot find it in twenty seconds,

your resume is yeeted and deleted.

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I wish that was not the case,

but unfortunately, that is

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just how little time recruiters

actually have to view your resume.

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So, uh, we'll-

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Avery Smith-2: I li- I like what

he just said, you know, 20 seconds.

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I think that's even…

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I think a lot of people

say it's like seven seconds

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Avery Smith's screen-2: I be able

to find that in twenty seconds?

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Probably not, 'cause they've been

doing this for eight months and

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has gotten zero interviews, so I'm

expecting to find nothing in this.

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But let's see how bad

this resume is, and go.

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Oh, my God.

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Uh, cannot use anything here.

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Cannot use anything here.

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Uh, so then we go down to

here is designed and built an

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executive reporting dashboard.

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Okay.

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Data quality, KPIs.

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Ha, this is irrelevant to leadership.

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Could assess status,

analyze, informed, hot dog.

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Uh, hot dog, hot dog,

hot dog, hot dog, uh-

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Avery Smith-2: And when he's saying

hot dog here, he explains this later

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in the episode, it's basically not what

he's looking for is what he's saying.

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. Um, kind of a weird way of

expressing it, but just, just say

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it's-- just think it's not good

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Avery Smith's screen-2: Time.

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Oh boy, this is a bad one.

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All right, I understand why

you're getting zero interviews.

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So there's a lot of this that's wrong,

and I'm gonna go through it one by one.

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So first things first, your

formatting is atrocious.

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This is the formatting you wanna use.

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You can find it in the link below.

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It's free.

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Use it.

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This is what it looks like.

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This is how it should be.

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This ain't it

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Avery Smith-2: So for those of you

who are listening via the podcast,

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he's, he's showing the resume on the

screen, and he's showing, you know,

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a template that he really likes.

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And I think the big thing for, for what

this resume is doing wrong and what

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he thinks they should be doing better

is essentially have more white space.

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Because this is like-- like the

professional summary is one, two,

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three, four, five, six, seven, eight.

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It's eight lines straight of just text

where you can't really scan it, and then

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it goes straight to core skills, uh, which

is just like a bunch of keyword stuffing.

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Um, and then even the bullet

points in the, uh, professional

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experience is pretty long.

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Like each bullet point looks to be

one, two, three lines, one, two,

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three lines, one, two, three lines.

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So it's just a lot of block text going

on, and it makes it really hard to

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scan anything in those twenty seconds.

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There's lots of information in

there, but it's not really digestible

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for someone like a recruiter

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Avery Smith's screen-2: This is bad.

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This is really, really, really bad.

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So first things first, your

formatting is atrocious.

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Uh, I don't know if you graduated or

not, I can't even find your degree, which

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is why your degree needs to be up here.

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Second off, um, nothing…

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There's a reason, like, if you look at

this, you're like, "Well, hold on, Lee.

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What are you talking about?"

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Everything you want is

in these two sections.

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Avery Smith-2: Just, just a note

where he's like, "I can't even

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tell if you've graduated or not."

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Well, if you've been a data and business

analyst with six-plus years translating

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complex operational data, you've either

been-- you know, you have a, a degree,

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like you landed a job, uh, with a

degree, or you landed a job without

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a degree and now you have experience.

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So I'm not sure why he's saying the

education section's so important.

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I know a lot of you guys listening

are career pivoters, and you have

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a degree, but it's not in data

analytics, it's not in statistics,

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it's not in computer science.

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Um, and so I don't necessarily think you

have to have it on the top of your resume.

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If it's helpful, sure, but like

eventually, your professional

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experience trumps your degree, right?

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Like my undergraduate degree

is in chemical engineering.

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I haven't really worked as a

chemical engineer for a long time.

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Like should I put that

on top of my resume?

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I don't think so

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Avery Smith's screen-2: Why did you

highlight all this stuff in red?

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This is exactly what you're looking for.

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And the answer to that is, "No, it's not.

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Uh, it's not what I'm looking for.

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I'm looking for

qualifications, not keywords."

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So if I was-

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Avery Smith-2: Now, n-notice

what he's saying here.

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I'm looking for

qualifications, not keywords.

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One important thing I would say is, while

an applicant tracking system, a lot of

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the times, is just looking for keywords.

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So just know that your

resume serves two purposes.

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One is convincing an applicant

tracking system that you're a worthy

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candidate, and two is convincing a

human that you're a worthy candidate,

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and those are two different tasks.

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Avery Smith's screen-2: If I was looking

for keywords, then yeah, everything

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here would be what I'm looking for.

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Power BI, Excel, Jira, SQL, R,

Python, uh, Databricks, uh, AWS.

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If I was keyword hunting, then a

skill section would be relevant.

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But I'm not keyword hunting,

I'm qualification hunting.

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And what a qualification is,

is a keyword plus, the plus

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is important, how you used it

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Avery Smith-2: Okay.

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: 00:12:47

So a, a qualification is a

keyword plus how you used it

299

: 00:12:51

Avery Smith's screen-2: Plus

where you used it, which is skills

300

: 00:12:53

Avery Smith-2: Plus where you used it

301

: 00:12:54

Avery Smith's screen-2: section,

professional summary, do not show.

302

: 00:12:56

And the non-technical reason

you did it to help the business.

303

: 00:12:59

Now

304

: 00:13:00

Avery Smith-2: Okay, so let's, let's,

let's go through that one more time.

305

: 00:13:02

So a qualification is a keyword plus

where you used it, plus how you used it,

306

: 00:13:07

and then what purpose you used it for.

307

: 00:13:09

Um, so I think what he's trying

to say is like, you know, SQL.

308

: 00:13:13

He wants to see SQL in

this role right here.

309

: 00:13:15

A bullet point like, you know, uh, used

SQL to, um, analyze four hundred thousand

310

: 00:13:24

rows of data to make ten thousand--

save ten thousand dollars in costing.

311

: 00:13:30

So it's the keyword and the where is

this Fortune five hundred company.

312

: 00:13:35

The how, I don't really know how.

313

: 00:13:36

It's like there's only one way to

really use SQL, I guess, like queries.

314

: 00:13:39

Uh, and then for what purpose?

315

: 00:13:41

To like save ten thousand dollars.

316

: 00:13:42

I think that's what he's

looking for, essentially.

317

: 00:13:45

Essentially, he's just saying that there's

just a bunch of keywords here, and he'd

318

: 00:13:48

rather see them spread out throughout

the resume and the experience section

319

: 00:13:52

and maybe even the professional summary

and maybe even the education on how

320

: 00:13:55

you're actually using those keywords

and why you're actually using them

321

: 00:13:59

Avery Smith's screen-2: But before you

go, "Lee, there's only two parts of any

322

: 00:14:01

job, which is make money, save money."

323

: 00:14:03

Yeah, that, that's this high level.

324

: 00:14:05

I need you to be here, right?

325

: 00:14:06

I, I don't wanna ta- I don't care about

this part, I care about this part.

326

: 00:14:09

I, I wanna know why you did what you did,

saved or w- uh, made the company money.

327

: 00:14:13

What was the purpose of your job?

328

: 00:14:15

I don't care that you conducted a deep

analysis of 65 legacy pipelines, reverse

329

: 00:14:19

engineering undocumented business rules,

transformation logged data dependencies,

330

: 00:14:21

across 9,000 processes, producing the

scope assessment and gap analysis, showed

331

: 00:14:24

executive alignment on migration strategy.

332

: 00:14:25

No, no, no, no, no, no, no, no.

333

: 00:14:26

I wanna know

334

: 00:14:27

Avery Smith-2: He's talking so fast.

335

: 00:14:28

Do I have, uh…

336

: 00:14:29

Oh, I do have 1.25

337

: 00:14:31

speed on.

338

: 00:14:31

Sorry, guys.

339

: 00:14:32

Uh, okay

340

: 00:14:34

Avery Smith's screen-2: You did that.

341

: 00:14:35

That's, that's too technical, right?

342

: 00:14:37

We've got why you did what you did,

which is we've got your job is to make

343

: 00:14:43

money or save money, and then we've

got whatever you wrote at the bottom.

344

: 00:14:46

I need you to meet me in the middle here.

345

: 00:14:47

All right?

346

: 00:14:47

That's what we're looking for in the why.

347

: 00:14:49

Uh-

348

: 00:14:50

Avery Smith-2: So here he's saying like,

of course, like you, you know, this

349

: 00:14:53

person did conduct deep dive analysis

of sixty-five legacy ETL pipelines.

350

: 00:14:58

You know, that's, that's

what their job was.

351

: 00:15:01

But it's too technical for this recruiter

to actually understand the purpose.

352

: 00:15:06

Plus, not only like, you

know, we don't care about what

353

: 00:15:10

you did, you care about why.

354

: 00:15:11

So why did you do it?

355

: 00:15:13

So they did it to produce the scope

assessment and gap analysis that drove

356

: 00:15:17

executive alignment on migration strategy.

357

: 00:15:20

Um, and that's just like,

you gotta be more specific.

358

: 00:15:23

Like, how did that save us

time or money, essentially?

359

: 00:15:25

Um, or like, did it save, you know, hours?

360

: 00:15:27

Did it save potential

errors in the future?

361

: 00:15:30

Try to give like a dollar

sign or a number of hours or

362

: 00:15:33

something like that right there

363

: 00:15:35

Avery Smith's screen-2: Um, and

there's so many random numbers.

364

: 00:15:38

This is also filled to the brim with

hot dogs, which I'm about to explain

365

: 00:15:43

This is the part where he explains

his hot dog analogy, which is

366

: 00:15:46

a bullet point that is a cheap

version of what he's trying to get.

367

: 00:15:51

It's impressive sounding, it's

technical, it's what you maybe

368

: 00:15:55

did, but it's not the full thing.

369

: 00:15:57

It's just like the keyword doesn't have

like the where and the what and the why.

370

: 00:16:01

Uh, the analogy fell a little bit flat

with me, so I will skip this part for you.

371

: 00:16:05

Avery Smith's screen-2: So

that is my problem here is all

372

: 00:16:06

this stuff is re-- tangentially

related to being a data analyst.

373

: 00:16:08

This is tangentially

related to what I want.

374

: 00:16:10

It is not what I want.

375

: 00:16:11

I want this.

376

: 00:16:12

So when you submit a resume that

looks like this and not this, what

377

: 00:16:15

actually happens on the part is

the recruiter looks and this goes,

378

: 00:16:18

boop, boop, boop, boop, boop.

379

: 00:16:19

This doesn't matter.

380

: 00:16:19

This doesn't matter.

381

: 00:16:20

This doesn't matter.

382

: 00:16:21

Doesn't matter.

383

: 00:16:21

Doesn't matter.

384

: 00:16:22

Avery Smith-2: I want you to pay close

attention to this because this is how a

385

: 00:16:24

recruiter actually sees your resume here.

386

: 00:16:26

Ready?

387

: 00:16:27

Here we go

388

: 00:16:28

Avery Smith's screen-2: Doesn't matter.

389

: 00:16:28

It doesn't matter.

390

: 00:16:28

It doesn't matter.

391

: 00:16:29

It doesn't matter.

392

: 00:16:29

It doesn't matter.

393

: 00:16:30

It doesn't matter.

394

: 00:16:30

It doesn't matter.

395

: 00:16:30

It doesn't matter.

396

: 00:16:31

It doesn't matter.

397

: 00:16:31

It doesn't matter.

398

: 00:16:32

It doesn't matter.

399

: 00:16:32

It doesn't matter.

400

: 00:16:33

It doesn't matter.

401

: 00:16:33

And then

402

: 00:16:34

Avery Smith-2: And for our audio

au- audience, he's essentially like

403

: 00:16:37

whitening out the entire resume.

404

: 00:16:39

He's basically saying none of

the resume is helpful right now

405

: 00:16:42

Avery Smith's screen-2: Then they go

down to here and they say, "Okay, cool.

406

: 00:16:43

Actually, what I'm maybe looking for."

407

: 00:16:45

And they say, "Okay, uh, I don't

know what you did, I don't know how

408

: 00:16:48

you did it, and I don't know why

you did it, so this doesn't count."

409

: 00:16:52

Avery Smith-2: Now he's scratching out,

uh, the first bullet point because he

410

: 00:16:54

feels like it doesn't say why he did it.

411

: 00:16:57

I mean, he's saying it doesn't say where.

412

: 00:16:59

Let's listen one more time,

'cause that makes no sense to me

413

: 00:17:01

Avery Smith's screen-2: And then they go

down to here and they say, "Okay, cool.

414

: 00:17:03

Actually, what I'm maybe looking for."

415

: 00:17:05

And they say, "Okay, uh,

I don't know what you did.

416

: 00:17:08

I don't know-

417

: 00:17:08

Avery Smith-2: Well, what you

did is right here, designed and

418

: 00:17:10

built an executive reporting

dashboard tracking pipeline health

419

: 00:17:15

Avery Smith's screen-2: How

you didn't, I don't know

420

: 00:17:16

Avery Smith-2: Uh, how you did it.

421

: 00:17:17

I mean, I guess y-- the--

this resume person should

422

: 00:17:20

have said what tool they used.

423

: 00:17:21

There's a really good w-- opportunity

to keyword stuff like, where'd you

424

: 00:17:23

build these reporting dashboards?

425

: 00:17:26

Avery Smith's screen-2: I know why

you did it, so this doesn't count

426

: 00:17:27

Avery Smith-2: Why you did it.

427

: 00:17:28

Let's see.

428

: 00:17:29

Um, analysis directly informed a decision

to extend a multi-million dollar project

429

: 00:17:34

timeline from four to six months.

430

: 00:17:37

Um, so I mean, that is why you did it.

431

: 00:17:39

So analysis, w-- I think, I think

maybe instead of changing the timeline,

432

: 00:17:44

it's like, well, what is that in

dollar values or what is that in risk?

433

: 00:17:47

Like, maybe th-this could have

just been more succinctly said.

434

: 00:17:51

So I would have probably said,

"Designed and built an executive

435

: 00:17:54

da-- reporting dashboard in Power

BI that tracks," Let's just say KPIs

436

: 00:17:59

That Changed a-- And I would--

Instead of doing multimillion

437

: 00:18:04

dollar, I would just put a dollar.

438

: 00:18:05

If you don't know what it is, it's,

is it more than ten or less than ten?

439

: 00:18:09

Uh, put seven.

440

: 00:18:10

And, you know, if it's

about twenty, put twenty.

441

: 00:18:13

So twenty million d- twenty million

dollar project, instead of saying four

442

: 00:18:17

to six months, I would say extended

by fifty percent to prevent, you

443

: 00:18:22

know, errors or something like that.

444

: 00:18:25

Um, that's how I think I'd make that

bullet point a little bit better

445

: 00:18:29

Avery Smith's screen-2: Okay.

446

: 00:18:29

Uh, I don't know what you did, I don't

know how you did it, and I don't know

447

: 00:18:32

why you did it, so this doesn't count.

448

: 00:18:34

Then

449

: 00:18:34

Avery Smith-2: I think that's very harsh.

450

: 00:18:35

I don't really get…

451

: 00:18:36

I mean, it could be a better bullet

for sure, but there's, there's

452

: 00:18:39

pieces of in there, of it in there

453

: 00:18:41

Avery Smith's screen-2: Look at

this and go, "Okay, uh, I don't know

454

: 00:18:43

what you did, how you did, or why

you did it, so this doesn't count."

455

: 00:18:46

And then you do this and you say,

"Yep, this is filled with hotdogs.

456

: 00:18:48

It's great that you define quality

standards and SL pipelines for 300K

457

: 00:18:51

records every ten to fifteen minutes,

but I'm looking for somebody that can

458

: 00:18:55

influence stakeholders and use Sequel.

459

: 00:18:57

That's not that.

460

: 00:18:58

You don't tell me how-

461

: 00:18:59

Avery Smith-2: So I, once again, I

think, I think this really shows that

462

: 00:19:03

recruiters aren't really-- They're

not trying to get you hired, right?

463

: 00:19:07

They're, they're not giving

you the benefit of the doubt.

464

: 00:19:09

You have to be 100% prepared.

465

: 00:19:13

This resume has to be 100%

ready to go with no exceptions,

466

: 00:19:16

no doubts, no issues at all.

467

: 00:19:18

Because if there's anything that's

suboptimal, a recruiter's just gonna

468

: 00:19:22

find it and say it sucks, okay?

469

: 00:19:25

Like, I hope this is giving you

a glimpse to literally how a real

470

: 00:19:28

recruiter looks at your resume

471

: 00:19:30

Avery Smith's screen-2: how you

did it, so this doesn't count,

472

: 00:19:32

and then this doesn't count.

473

: 00:19:33

I think I actually missed

something that did count.

474

: 00:19:34

Uh, I, I ran out of time, so I didn't

475

: 00:19:37

Avery Smith-2: I think I missed

something that did count.

476

: 00:19:39

See?

477

: 00:19:39

And, and he even recognizes it here.

478

: 00:19:41

He's like, "Wait, actually one of

those bullets wasn't that bad."

479

: 00:19:44

But the problem is, is he's already

given up on this resume after those

480

: 00:19:47

20 seconds, and you made him work.

481

: 00:19:49

The harder you make him work to actually

find the gold in your resume, you just--

482

: 00:19:53

the chances just go down exponentially.

483

: 00:19:55

So you gotta be really

solid with your resume

484

: 00:19:58

Avery Smith's screen-2: Go past this.

485

: 00:20:00

So when you are making your resume,

I want you to make it for Bob.

486

: 00:20:04

Bob is a senior manager at

Headless Headhunters Hamburger Hut.

487

: 00:20:08

Bob is the CEO.

488

: 00:20:10

Bob is the one that decides

if you get a job or not

489

: 00:20:13

Avery Smith-2: I mean, why are we making,

why are we making a resume for a CEO?

490

: 00:20:18

CEOs won't be hiring you.

491

: 00:20:20

It'll be a hiring manager, right?

492

: 00:20:23

Like, I don't get why he's saying this.

493

: 00:20:25

Let's, let's see if he can explain it

494

: 00:20:27

Avery Smith's screen-2: Bob is the hiring

manager and the recruiter wrapped into one

495

: 00:20:31

Avery Smith-2: I thought he was the CEO.

496

: 00:20:32

Which one is he?

497

: 00:20:34

Avery Smith's screen-2: Bob

cannot turn on their monitor.

498

: 00:20:36

You

499

: 00:20:37

Avery Smith-2: I mean, that's--

I think for most data analyst

500

: 00:20:40

hiring managers, that's very rude.

501

: 00:20:42

Like, they're very technically sound.

502

: 00:20:44

Like, they're more

technically sound than you.

503

: 00:20:46

Maybe he's just saying this because he

feels this way about, like, tech and data.

504

: 00:20:50

Like, as a recruiter, he doesn't

know a whole lot about data and

505

: 00:20:54

tech, and so we need to write

our resumes for the recruiter?

506

: 00:20:58

'Cause hiring managers, they're

decent most of the time.

507

: 00:21:00

They're not gonna be, you know,

they're not, they're not, like,

508

: 00:21:04

super in the weeds with, you know,

tech and data and stuff like that.

509

: 00:21:07

But most of the time,

they're pretty dang good.

510

: 00:21:09

Like, they've worked as individual

contributors in that role before.

511

: 00:21:13

It might have been 10 years ago, but

they still kinda know what's going on

512

: 00:21:18

Avery Smith's screen-2: You need to make

your resume enough that Bob can understand

513

: 00:21:21

what you do, and he needs to find this.

514

: 00:21:24

If you don't, you will get rejected.

515

: 00:21:25

Is that fair?

516

: 00:21:26

No, it's not fair.

517

: 00:21:27

But unfortunately, neither is life.

518

: 00:21:30

Like, if, if, if life was fair,

you wouldn't come across this

519

: 00:21:34

channel in the first place

520

: 00:21:36

Avery Smith-2: I think that is a really

good point that, like, this, this sucks.

521

: 00:21:39

The fact that the, the recruiter

looks at a resume this way sucks.

522

: 00:21:42

The fact that it's so hard to

land a job right now, it sucks.

523

: 00:21:45

Um, and it's not fair, and it's not how

it should be, but that's just the system

524

: 00:21:48

we're in now, and you have two choices.

525

: 00:21:50

One, you can play the game and

try to actually, you know, get

526

: 00:21:56

interviews and get hired, or two,

you can get frustrated and give up.

527

: 00:21:59

Those are your two options.

528

: 00:22:00

Um, and be like, "This

is, this isn't fair.

529

: 00:22:02

I give up."

530

: 00:22:03

yeah, it does suck, but giving

up's not a good option either.

531

: 00:22:06

Giving up sucks too.

532

: 00:22:08

So choose your hard.

533

: 00:22:09

You either have the hard of making a good

resume and, and getting it in front of

534

: 00:22:13

recruiters and hiring managers, or you

have the hard of you don't get a data

535

: 00:22:16

job and you-- maybe you don't get a job.

536

: 00:22:18

Both of those options are hard.

537

: 00:22:19

It's just different hard

538

: 00:22:20

Avery Smith's screen-2: Uh, also

there is a critical error that I did

539

: 00:22:22

notice here is never ever do this.

540

: 00:22:25

Uh, this, never ever do this right here.

541

: 00:22:28

Um, I'm gonna give y'all a

second to figure out what's

542

: 00:22:32

wrong with this, but this is

543

: 00:22:33

Avery Smith-2: For our audio audience,

he is circling the dates for each

544

: 00:22:37

one of the jobs in the professional

experience section, and they say twenty

545

: 00:22:41

twenty-four to twenty twenty-five and

twenty twenty-four to twenty twenty-four.

546

: 00:22:44

So he has no dates.

547

: 00:22:45

He or she has no dates on their resume.

548

: 00:22:47

Um, and sorry, no months.

549

: 00:22:49

You need to have months on your

resume, um, because basically

550

: 00:22:52

having no months can be a red flag

551

: 00:22:55

Avery Smith's screen-2: that, in fact,

that entire thing I would remove.

552

: 00:22:56

I wouldn't even put this on here.

553

: 00:22:58

That's just gonna make you

look like a job hopper.

554

: 00:22:59

Like, not even counting the fact

that your resume has nothing in it.

555

: 00:23:02

Again, this is not the worst resume

I've seen in my life, but it's

556

: 00:23:04

Avery Smith-2: This resume has

nothing in it, but it's not the

557

: 00:23:07

worst resume he's seen in his life.

558

: 00:23:09

So, uh, that feels like an

oxymoron sentence right there.

559

: 00:23:13

Um, by the way, he's currently whiting

out this job that was from: 2024

560

: 00:23:18

because it makes this person look like a

job hopper or wasn't at the job very long.

561

: 00:23:21

Also I'm assuming the, the companies they

work for, it says Fortune 500 automotive

562

: 00:23:26

client and Fortune 500 utility provider.

563

: 00:23:29

I'm assuming those actually have the

company names in the actual resume, um,

564

: 00:23:34

because down below it says JPMorgan Chase.

565

: 00:23:37

If not, that's-- I mean, you gotta

put the company you work for.

566

: 00:23:40

You can't just say, "I worked

for a mystery company."

567

: 00:23:43

Like that's not good.

568

: 00:23:45

Like I don't know if this is how

he asks for, um, if, if he asks for

569

: 00:23:50

resumes this way to be like a little

bit more protected and anonymized.

570

: 00:23:54

I don't know.

571

: 00:23:55

But, uh, I don't think that's great

572

: 00:23:56

Avery Smith's screen-2: Not

even counting the fact that

573

: 00:23:58

your resume has nothing in it.

574

: 00:23:59

Again, this is not the worst resume I've

seen in my life, but it's very, very bad.

575

: 00:24:03

Uh

576

: 00:24:03

Avery Smith-2: It's, it's probably like a

four out of 10, maybe a three out of 10.

577

: 00:24:07

It's not that bad.

578

: 00:24:09

It's not that bad.

579

: 00:24:10

Um, it's just wordy, no white

space, and yeah, poorly formatted

580

: 00:24:16

Avery Smith's screen-2: Uh, and

then down here, again, that could

581

: 00:24:19

be December twenty twenty-three

to January twenty twenty-four.

582

: 00:24:22

I don't know.

583

: 00:24:22

You need the months.

584

: 00:24:23

This looks bad.

585

: 00:24:24

Always, always, always.

586

: 00:24:25

But that's all I can do for this resume

587

: 00:24:26

Hopefully that gave you a good idea

of how you could improve your very own

588

: 00:24:30

resume to start to get more interviews.

589

: 00:24:32

If you want a blank slate and you want

a template that has been proven year

590

: 00:24:36

after year, I'll have a link in the

description down below, or you can

591

: 00:24:39

go to datacareerjumpstart.com/resume

592

: 00:24:42

and download that for absolutely free