255: Why This Data Analyst Got 0 Interviews (According to a Recruiter)
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Real recruiter spends 20 seconds on this resume and finds nothing worth keeping. I show you why.
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📺 Original resume review by Headless Headhunter 👉 https://youtu.be/iLjHV8VPzK8
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⌚ TIMESTAMPS
00:00 – Eight months, zero interviews
08:45 – Make it scannable
09:45 – The formatting problem
12:09 – Your resume has two jobs
16:12 – How fast they give up
20:18 – Hiring managers aren't clueless
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Transcript
So this data analyst has
gotten zero interviews in eight
2
:months of applying for jobs.
3
:So we're gonna talk about why this
is the case and how they can fix
4
:it and how you can fix it if you're
struggling to land interviews
5
:If you are struggling to land
interviews, it's not you.
6
:You're not the problem.
7
:It's likely something that's
either your LinkedIn or your resume
8
:that's not optimized, that is not
actually getting you in front of
9
:hiring managers and recruiters.
10
:It's not getting you past the
applicant tracking system,
11
:and it's keeping you stuck.
12
:You might think that you suck.
13
:You might think that your skills suck.
14
:You might think that you're not cut
out for data analytics, but you are.
15
:You just need a good resume.
16
:And today, we're gonna be
looking at a not so great resume
17
:and how we can make it better
18
:one of the easiest ways to make
your resume better is just to
19
:start with a really good template
with built-in good structure.
20
:So I actually have a free template
for you to download and just use, and
21
:just trust me, I have been helping
people land data jobs for five years.
22
:This resume really works.
23
:You can go to
datacareerjumpshot.com/resume
24
:or find the link in the show notes
down below to get that resume.
25
:this resume is going
to do wonders for you.
26
:It's gonna save you a lot
of time, and it's 100% free.
27
:So go grab it right now
28
:We'll be reacting to a video that was
done by a gentleman named Headless
29
:Headhunter, that is a mouthful, uh,
from a data analyst resume that he got
30
:submitted to him on his YouTube channel.
31
:I'll have a link to his channel in the
description down below, and if you're
32
:listening on the audio podcast, I'm
gonna try to do my best to describe what
33
:this resume looks like and what we are
looking at throughout the entire process
34
:Avery Smith-2: All right, here we go
35
:Avery Smith's screen-2: Recruiter
here to review your resumes.
36
:The resume we have up first is a
data analyst, and this person has
37
:been applying for eight months
and has gotten zero interviews.
38
:So this is a real stinker of a resume,
and I'm here to tell you why it's bad,
39
:how to fix it so that you can get a job
40
:Avery Smith-2: Just one note, just
because, you know, they haven't been
41
:able to land a job in eight months
doesn't exactly mean the resume is bad.
42
:That's probably one of
the things it could be.
43
:But if you only applied for eight
jobs in eight months, you're also
44
:not likely to get any interviews.
45
:So it also depends on, you know, how
many applications you've sent out.
46
:If you sent out, you know, hundreds of
applications and you have no interviews,
47
:then yes, definitely a problem.
48
:But just remember that, like,
it's not just your resume,
49
:uh, that gets you interviews
50
:Avery Smith's screen-2: Uh, that is
my job as the headless headhunter.
51
:So what do we need as a data analyst?
52
:We need degree, industry, SQL
53
:Avery Smith-2: Okay, so he's going
over the qualifications that a
54
:data analyst needs, needs, and
he's saying degree in industry.
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:I don't know what industry means.
56
:Uh, degree, yeah, you can argue like
having a degree is helpful, but I don't
57
:know if he means a data analytics degree
'cause there's not very many of those.
58
:I don't have one of those.
59
:Um, so I'm not sure what he means there.
60
:But yes, there are m- there's not a
ton of data jobs that you can land
61
: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.
64
:Influence stakeholders.
65
:Explain technical concepts
to non-technical people and
66
:non-technical stakeholders.
67
:You absolutely need that.
68
:Your job is to convince Bob, who
cannot turn on his monitor, why you
69
: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
71
:Avery Smith-2: Uh, very important here
that like, yes, being a clear communicator
72
:is an important job as a data analyst.
73
: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.
75
:Um, I know he's just going this off
the cuff where he's like, "You need to
76
:explain to Bob why we're using SQL."
77
:And to be honest, most of the time you'll
be working at a larger company that
78
:already decided they're using SQL, and
it's not really your job as like a junior
79
:or intermediate data analyst to be like,
"We should switch to something else."
80
:And to be honest, what
are you gonna switch to?
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:Like everyone uses SQL.
82
:So didn't love his example here.
83
:I'm being nitpicky, but I just
wanna, you know, bring up a point
84
:here that recruiters, they have to
know everyone's job inside and out,
85
:and that's absolutely impossible.
86
:So like just know that recruiters kinda
know what they're talking about, but
87
:not exactly, because he has to know all
the details of a data analyst, of an
88
:accountant, of a financial professional.
89
:Uh, you know, maybe he
does nursing, I don't know.
90
:Like rec-recruiters work for so many
different roles that they have to know,
91
: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
93
:with data, not what the data is.
94
:This is another big one that data
analysts get wrong all the time.
95
:Also, I do more than tech resumes, such
as like software engineers, data analysts.
96
:I do like accountants and everything
else, but the tech market is so incredibly
97
:terrible that like 50% of the resumes sent
to me are tech, but I do do more than tech
98
:Avery Smith-2: So once again, he's
just proving the point here that
99
:recruiters, they often do lots of
different types of roles, and it's
100
:impossible for them to know, you
know, the ins and outs of every role.
101
:So when you're, when you're-- when
recruiters are reviewing your resume,
102
:they don't know everything, and so you
have to work really hard to try to make
103
:it as easy as possible for them to know
what you're talking about, and we'll,
104
:we'll get to this here in a second
105
: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
107
:Avery Smith-2: I just
don't think that's true.
108
:Like, how many of you guys…
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:Let me know in the Spotify
comments and the YouTube comments
110
:that, like, are you putting how
you got the data on your resume?
111
:I think a lot of data analysts don't
talk about how they got the data.
112
:I think they talk about
how they analyze the data.
113
:A lot of the times, data
analysts don't get data.
114
:Uh, like it's already in a database,
so why would they, you know, list
115
:that as one of their responsibilities
or one of their bullet points?
116
:And if you did get the data on your
own, I think that's really impressive
117
:because getting data is really difficult.
118
:Like, if you web scraped data from
the internet, you know, that's hard
119
:to do, and that deserves a bullet
point, and that's useful for a lot of
120
:different industries, a lot of different
companies, a lot of different jobs.
121
:Um, so I don't really get why he's
saying, like, "Don't talk about, you
122
:know, where you got the data from."
123
:I don't think we are.
124
:And two, if you did get the
data in a unique way, I think
125
:that's worth pointing out.
126
:So I don't, I don't get his point here
127
:Avery Smith's screen-2: No one cares
if you used a Monte Carlo simulation
128
:to flip-flop the floop flop and
the blah, blah, blah, blah, blah.
129
:All they-
130
:Avery Smith-2: I, I think if you listen
carefully, he's like, "No one cares
131
:if you did a Monte Carlo simulation,"
which is one way to generate data.
132
:Like, if you don't have actual
data, you can simulate data, you
133
:know, and run, you know, hundreds,
thousands of different simulations.
134
:Monte Carlo is one, one way to do it.
135
:Um, but where he's just like flip-flop,
blah, blah, blah, blah, blah.
136
:I think that's what recruiters read when
they see a data analyst resume, is they
137
:see like one word they know, like Monte
Carlo simulation, and then it's just like
138
:blah, blah, blah, blah, blah, and it's
just like a bunch of jargon for them.
139
:So you just have to keep that in mind,
that recruiters don't know all of
140
:data analytics jargon that's going on
141
:Avery Smith's screen-2: They care about is
what you did with it, not how you got it
142
:Avery Smith-2: I do think this point is
really important, that what you do with
143
:the data is the most important thing.
144
:It is more important
than, than how you got it.
145
:Um, and we're not just analyzing
data for analyzing data's sake.
146
:We're not doing it for funsies.
147
:We're doing it for a purpose.
148
:And so it's really important to try to
illustrate why you did what you did.
149
:Like, how are you helping the
business in the big picture?
150
:Avery Smith's screen-2: I need to
see how you solved a problem with
151
:the data, not what the data is.
152
:I need to see MS Excel, yes, really,
macros, pivot tables, VLOOKUP,
153
:Word, Python, R, multiple projects
and deadlines, and nice to have is
154
:cloud AI and security clearances
155
:Avery Smith-2: So, uh, he
kind of went through the
156
:qualifications a little bit more.
157
:He said Microsoft Excel, macros,
pivot tables, and VLOOKUP.
158
:So I think this is really funny because
number one, I don't really think a lot
159
:of people are using macros anymore.
160
:They've always kind of sucked.
161
:Um, they take a lot of effort to code,
and they're very slow and not very robust.
162
:Uh, I think Python in Excel has
really taken over most macros.
163
:Uh, pivot tables are still the king.
164
:We still use a lot of pivot tables.
165
:Now, notice VLOOKUP here, and all of
you guys probably who are listening
166
:and, you know, have touched, you
know, Excel, you're like, "Oh, I
167
:know VLOOKUP, but XLOOKUP's way
better or INDEX MATCH is way better."
168
:And yes, that might be true, but my
point here is, remember recruiters,
169
:they don't know the difference between
VLOOKUP and XLOOKUP like you do.
170
:And if you're unfamiliar with it, it's
basically the exact same thing in Excel
171
:except for XLOOKUP's a lot easier.
172
:VLOOKUP, you have to like be a little
bit more specific with what, what data
173
:you're actually trying to look up.
174
:It's basically a way to search a
large data set, and if you know key--
175
:one key value, you can get its pair.
176
:Um, but my point here is like they're
looking for VLO-- the recruiter's
177
:looking for VLOOKUP on your resume.
178
:Um, and maybe even an applicant
tracking system, ATS is, is as well.
179
:So even though you might not use
VLOOKUP and you know XLOOKUP is
180
:better, it might be worth having
things like VLOOKUP on your resume.
181
:Um, also, I don't know when he's
saying, uh, bullet point of Python/R,
182
:multiple projects and deadlines.
183
:I think deadlines is interesting.
184
:I don't know.
185
:He didn't really expand on that.
186
:I think that's interesting.
187
:Nice to have Cloud.
188
:Yeah, it's nice to have.
189
:It's not listed in very many,
uh, data job descriptions.
190
:AI, this is in twelve percent now.
191
:Uh, security clearance, obviously,
that's not something you can
192
:really just go out there and get.
193
:So, um, those are some nice to haves.
194
:Avery Smith's screen-2: So
I only have twenty seconds
195
:to find what I need to find.
196
:If I cannot find it in twenty seconds,
your resume is yeeted and deleted.
197
:I wish that was not the case,
but unfortunately, that is
198
:just how little time recruiters
actually have to view your resume.
199
:So, uh, we'll-
200
:Avery Smith-2: I li- I like what
he just said, you know, 20 seconds.
201
:I think that's even…
202
:I think a lot of people
say it's like seven seconds
203
:Avery Smith's screen-2: I be able
to find that in twenty seconds?
204
:Probably not, 'cause they've been
doing this for eight months and
205
:has gotten zero interviews, so I'm
expecting to find nothing in this.
206
:But let's see how bad
this resume is, and go.
207
:Oh, my God.
208
:Uh, cannot use anything here.
209
:Cannot use anything here.
210
:Uh, so then we go down to
here is designed and built an
211
:executive reporting dashboard.
212
:Okay.
213
:Data quality, KPIs.
214
:Ha, this is irrelevant to leadership.
215
:Could assess status,
analyze, informed, hot dog.
216
:Uh, hot dog, hot dog,
hot dog, hot dog, uh-
217
:Avery Smith-2: And when he's saying
hot dog here, he explains this later
218
:in the episode, it's basically not what
he's looking for is what he's saying.
219
:. Um, kind of a weird way of
expressing it, but just, just say
220
:it's-- just think it's not good
221
:Avery Smith's screen-2: Time.
222
:Oh boy, this is a bad one.
223
:All right, I understand why
you're getting zero interviews.
224
:So there's a lot of this that's wrong,
and I'm gonna go through it one by one.
225
:So first things first, your
formatting is atrocious.
226
:This is the formatting you wanna use.
227
:You can find it in the link below.
228
:It's free.
229
:Use it.
230
:This is what it looks like.
231
:This is how it should be.
232
:This ain't it
233
:Avery Smith-2: So for those of you
who are listening via the podcast,
234
:he's, he's showing the resume on the
screen, and he's showing, you know,
235
:a template that he really likes.
236
:And I think the big thing for, for what
this resume is doing wrong and what
237
:he thinks they should be doing better
is essentially have more white space.
238
:Because this is like-- like the
professional summary is one, two,
239
:three, four, five, six, seven, eight.
240
:It's eight lines straight of just text
where you can't really scan it, and then
241
:it goes straight to core skills, uh, which
is just like a bunch of keyword stuffing.
242
:Um, and then even the bullet
points in the, uh, professional
243
:experience is pretty long.
244
:Like each bullet point looks to be
one, two, three lines, one, two,
245
:three lines, one, two, three lines.
246
:So it's just a lot of block text going
on, and it makes it really hard to
247
:scan anything in those twenty seconds.
248
:There's lots of information in
there, but it's not really digestible
249
:for someone like a recruiter
250
:Avery Smith's screen-2: This is bad.
251
:This is really, really, really bad.
252
:So first things first, your
formatting is atrocious.
253
:Uh, I don't know if you graduated or
not, I can't even find your degree, which
254
:is why your degree needs to be up here.
255
:Second off, um, nothing…
256
:There's a reason, like, if you look at
this, you're like, "Well, hold on, Lee.
257
:What are you talking about?"
258
:Everything you want is
in these two sections.
259
:Avery Smith-2: Just, just a note
where he's like, "I can't even
260
:tell if you've graduated or not."
261
:Well, if you've been a data and business
analyst with six-plus years translating
262
:complex operational data, you've either
been-- you know, you have a, a degree,
263
:like you landed a job, uh, with a
degree, or you landed a job without
264
:a degree and now you have experience.
265
:So I'm not sure why he's saying the
education section's so important.
266
:I know a lot of you guys listening
are career pivoters, and you have
267
:a degree, but it's not in data
analytics, it's not in statistics,
268
:it's not in computer science.
269
:Um, and so I don't necessarily think you
have to have it on the top of your resume.
270
:If it's helpful, sure, but like
eventually, your professional
271
:experience trumps your degree, right?
272
:Like my undergraduate degree
is in chemical engineering.
273
:I haven't really worked as a
chemical engineer for a long time.
274
:Like should I put that
on top of my resume?
275
:I don't think so
276
:Avery Smith's screen-2: Why did you
highlight all this stuff in red?
277
:This is exactly what you're looking for.
278
:And the answer to that is, "No, it's not.
279
:Uh, it's not what I'm looking for.
280
:I'm looking for
qualifications, not keywords."
281
:So if I was-
282
:Avery Smith-2: Now, n-notice
what he's saying here.
283
:I'm looking for
qualifications, not keywords.
284
:One important thing I would say is, while
an applicant tracking system, a lot of
285
:the times, is just looking for keywords.
286
:So just know that your
resume serves two purposes.
287
:One is convincing an applicant
tracking system that you're a worthy
288
:candidate, and two is convincing a
human that you're a worthy candidate,
289
:and those are two different tasks.
290
:Avery Smith's screen-2: If I was looking
for keywords, then yeah, everything
291
:here would be what I'm looking for.
292
:Power BI, Excel, Jira, SQL, R,
Python, uh, Databricks, uh, AWS.
293
:If I was keyword hunting, then a
skill section would be relevant.
294
:But I'm not keyword hunting,
I'm qualification hunting.
295
:And what a qualification is,
is a keyword plus, the plus
296
:is important, how you used it
297
:Avery Smith-2: Okay.
298
:So a, a qualification is a
keyword plus how you used it
299
:Avery Smith's screen-2: Plus
where you used it, which is skills
300
:Avery Smith-2: Plus where you used it
301
:Avery Smith's screen-2: section,
professional summary, do not show.
302
:And the non-technical reason
you did it to help the business.
303
:Now
304
:Avery Smith-2: Okay, so let's, let's,
let's go through that one more time.
305
:So a qualification is a keyword plus
where you used it, plus how you used it,
306
:and then what purpose you used it for.
307
:Um, so I think what he's trying
to say is like, you know, SQL.
308
:He wants to see SQL in
this role right here.
309
:A bullet point like, you know, uh, used
SQL to, um, analyze four hundred thousand
310
:rows of data to make ten thousand--
save ten thousand dollars in costing.
311
:So it's the keyword and the where is
this Fortune five hundred company.
312
:The how, I don't really know how.
313
:It's like there's only one way to
really use SQL, I guess, like queries.
314
:Uh, and then for what purpose?
315
:To like save ten thousand dollars.
316
:I think that's what he's
looking for, essentially.
317
:Essentially, he's just saying that there's
just a bunch of keywords here, and he'd
318
:rather see them spread out throughout
the resume and the experience section
319
:and maybe even the professional summary
and maybe even the education on how
320
:you're actually using those keywords
and why you're actually using them
321
:Avery Smith's screen-2: But before you
go, "Lee, there's only two parts of any
322
:job, which is make money, save money."
323
:Yeah, that, that's this high level.
324
:I need you to be here, right?
325
:I, I don't wanna ta- I don't care about
this part, I care about this part.
326
:I, I wanna know why you did what you did,
saved or w- uh, made the company money.
327
:What was the purpose of your job?
328
:I don't care that you conducted a deep
analysis of 65 legacy pipelines, reverse
329
:engineering undocumented business rules,
transformation logged data dependencies,
330
:across 9,000 processes, producing the
scope assessment and gap analysis, showed
331
:executive alignment on migration strategy.
332
:No, no, no, no, no, no, no, no.
333
:I wanna know
334
:Avery Smith-2: He's talking so fast.
335
:Do I have, uh…
336
:Oh, I do have 1.25
337
:speed on.
338
:Sorry, guys.
339
:Uh, okay
340
:Avery Smith's screen-2: You did that.
341
:That's, that's too technical, right?
342
:We've got why you did what you did,
which is we've got your job is to make
343
:money or save money, and then we've
got whatever you wrote at the bottom.
344
:I need you to meet me in the middle here.
345
:All right?
346
:That's what we're looking for in the why.
347
:Uh-
348
:Avery Smith-2: So here he's saying like,
of course, like you, you know, this
349
:person did conduct deep dive analysis
of sixty-five legacy ETL pipelines.
350
:You know, that's, that's
what their job was.
351
:But it's too technical for this recruiter
to actually understand the purpose.
352
:Plus, not only like, you
know, we don't care about what
353
:you did, you care about why.
354
:So why did you do it?
355
:So they did it to produce the scope
assessment and gap analysis that drove
356
:executive alignment on migration strategy.
357
:Um, and that's just like,
you gotta be more specific.
358
:Like, how did that save us
time or money, essentially?
359
:Um, or like, did it save, you know, hours?
360
:Did it save potential
errors in the future?
361
:Try to give like a dollar
sign or a number of hours or
362
:something like that right there
363
:Avery Smith's screen-2: Um, and
there's so many random numbers.
364
:This is also filled to the brim with
hot dogs, which I'm about to explain
365
:This is the part where he explains
his hot dog analogy, which is
366
:a bullet point that is a cheap
version of what he's trying to get.
367
:It's impressive sounding, it's
technical, it's what you maybe
368
:did, but it's not the full thing.
369
:It's just like the keyword doesn't have
like the where and the what and the why.
370
:Uh, the analogy fell a little bit flat
with me, so I will skip this part for you.
371
:Avery Smith's screen-2: So
that is my problem here is all
372
:this stuff is re-- tangentially
related to being a data analyst.
373
:This is tangentially
related to what I want.
374
:It is not what I want.
375
:I want this.
376
:So when you submit a resume that
looks like this and not this, what
377
:actually happens on the part is
the recruiter looks and this goes,
378
:boop, boop, boop, boop, boop.
379
:This doesn't matter.
380
:This doesn't matter.
381
:This doesn't matter.
382
:Doesn't matter.
383
:Doesn't matter.
384
:Avery Smith-2: I want you to pay close
attention to this because this is how a
385
:recruiter actually sees your resume here.
386
:Ready?
387
:Here we go
388
:Avery Smith's screen-2: Doesn't matter.
389
:It doesn't matter.
390
:It doesn't matter.
391
:It doesn't matter.
392
:It doesn't matter.
393
:It doesn't matter.
394
:It doesn't matter.
395
:It doesn't matter.
396
:It doesn't matter.
397
:It doesn't matter.
398
:It doesn't matter.
399
:It doesn't matter.
400
:It doesn't matter.
401
:And then
402
:Avery Smith-2: And for our audio
au- audience, he's essentially like
403
:whitening out the entire resume.
404
:He's basically saying none of
the resume is helpful right now
405
:Avery Smith's screen-2: Then they go
down to here and they say, "Okay, cool.
406
:Actually, what I'm maybe looking for."
407
:And they say, "Okay, uh, I don't
know what you did, I don't know how
408
:you did it, and I don't know why
you did it, so this doesn't count."
409
:Avery Smith-2: Now he's scratching out,
uh, the first bullet point because he
410
:feels like it doesn't say why he did it.
411
:I mean, he's saying it doesn't say where.
412
:Let's listen one more time,
'cause that makes no sense to me
413
:Avery Smith's screen-2: And then they go
down to here and they say, "Okay, cool.
414
:Actually, what I'm maybe looking for."
415
:And they say, "Okay, uh,
I don't know what you did.
416
:I don't know-
417
:Avery Smith-2: Well, what you
did is right here, designed and
418
:built an executive reporting
dashboard tracking pipeline health
419
:Avery Smith's screen-2: How
you didn't, I don't know
420
:Avery Smith-2: Uh, how you did it.
421
:I mean, I guess y-- the--
this resume person should
422
:have said what tool they used.
423
:There's a really good w-- opportunity
to keyword stuff like, where'd you
424
:build these reporting dashboards?
425
:Avery Smith's screen-2: I know why
you did it, so this doesn't count
426
:Avery Smith-2: Why you did it.
427
:Let's see.
428
:Um, analysis directly informed a decision
to extend a multi-million dollar project
429
:timeline from four to six months.
430
:Um, so I mean, that is why you did it.
431
:So analysis, w-- I think, I think
maybe instead of changing the timeline,
432
:it's like, well, what is that in
dollar values or what is that in risk?
433
:Like, maybe th-this could have
just been more succinctly said.
434
:So I would have probably said,
"Designed and built an executive
435
:da-- reporting dashboard in Power
BI that tracks," Let's just say KPIs
436
:That Changed a-- And I would--
Instead of doing multimillion
437
:dollar, I would just put a dollar.
438
:If you don't know what it is, it's,
is it more than ten or less than ten?
439
:Uh, put seven.
440
:And, you know, if it's
about twenty, put twenty.
441
:So twenty million d- twenty million
dollar project, instead of saying four
442
:to six months, I would say extended
by fifty percent to prevent, you
443
:know, errors or something like that.
444
:Um, that's how I think I'd make that
bullet point a little bit better
445
:Avery Smith's screen-2: Okay.
446
:Uh, I don't know what you did, I don't
know how you did it, and I don't know
447
:why you did it, so this doesn't count.
448
:Then
449
:Avery Smith-2: I think that's very harsh.
450
:I don't really get…
451
:I mean, it could be a better bullet
for sure, but there's, there's
452
:pieces of in there, of it in there
453
:Avery Smith's screen-2: Look at
this and go, "Okay, uh, I don't know
454
:what you did, how you did, or why
you did it, so this doesn't count."
455
:And then you do this and you say,
"Yep, this is filled with hotdogs.
456
:It's great that you define quality
standards and SL pipelines for 300K
457
:records every ten to fifteen minutes,
but I'm looking for somebody that can
458
:influence stakeholders and use Sequel.
459
:That's not that.
460
:You don't tell me how-
461
:Avery Smith-2: So I, once again, I
think, I think this really shows that
462
:recruiters aren't really-- They're
not trying to get you hired, right?
463
:They're, they're not giving
you the benefit of the doubt.
464
:You have to be 100% prepared.
465
:This resume has to be 100%
ready to go with no exceptions,
466
:no doubts, no issues at all.
467
:Because if there's anything that's
suboptimal, a recruiter's just gonna
468
:find it and say it sucks, okay?
469
:Like, I hope this is giving you
a glimpse to literally how a real
470
:recruiter looks at your resume
471
:Avery Smith's screen-2: how you
did it, so this doesn't count,
472
:and then this doesn't count.
473
:I think I actually missed
something that did count.
474
:Uh, I, I ran out of time, so I didn't
475
:Avery Smith-2: I think I missed
something that did count.
476
:See?
477
:And, and he even recognizes it here.
478
:He's like, "Wait, actually one of
those bullets wasn't that bad."
479
:But the problem is, is he's already
given up on this resume after those
480
:20 seconds, and you made him work.
481
:The harder you make him work to actually
find the gold in your resume, you just--
482
:the chances just go down exponentially.
483
:So you gotta be really
solid with your resume
484
:Avery Smith's screen-2: Go past this.
485
:So when you are making your resume,
I want you to make it for Bob.
486
:Bob is a senior manager at
Headless Headhunters Hamburger Hut.
487
:Bob is the CEO.
488
:Bob is the one that decides
if you get a job or not
489
:Avery Smith-2: I mean, why are we making,
why are we making a resume for a CEO?
490
:CEOs won't be hiring you.
491
:It'll be a hiring manager, right?
492
:Like, I don't get why he's saying this.
493
:Let's, let's see if he can explain it
494
:Avery Smith's screen-2: Bob is the hiring
manager and the recruiter wrapped into one
495
:Avery Smith-2: I thought he was the CEO.
496
:Which one is he?
497
:Avery Smith's screen-2: Bob
cannot turn on their monitor.
498
:You
499
:Avery Smith-2: I mean, that's--
I think for most data analyst
500
:hiring managers, that's very rude.
501
:Like, they're very technically sound.
502
:Like, they're more
technically sound than you.
503
:Maybe he's just saying this because he
feels this way about, like, tech and data.
504
:Like, as a recruiter, he doesn't
know a whole lot about data and
505
:tech, and so we need to write
our resumes for the recruiter?
506
:'Cause hiring managers, they're
decent most of the time.
507
:They're not gonna be, you know,
they're not, they're not, like,
508
:super in the weeds with, you know,
tech and data and stuff like that.
509
:But most of the time,
they're pretty dang good.
510
:Like, they've worked as individual
contributors in that role before.
511
:It might have been 10 years ago, but
they still kinda know what's going on
512
:Avery Smith's screen-2: You need to make
your resume enough that Bob can understand
513
:what you do, and he needs to find this.
514
:If you don't, you will get rejected.
515
:Is that fair?
516
:No, it's not fair.
517
:But unfortunately, neither is life.
518
:Like, if, if, if life was fair,
you wouldn't come across this
519
:channel in the first place
520
:Avery Smith-2: I think that is a really
good point that, like, this, this sucks.
521
:The fact that the, the recruiter
looks at a resume this way sucks.
522
:The fact that it's so hard to
land a job right now, it sucks.
523
:Um, and it's not fair, and it's not how
it should be, but that's just the system
524
:we're in now, and you have two choices.
525
:One, you can play the game and
try to actually, you know, get
526
:interviews and get hired, or two,
you can get frustrated and give up.
527
:Those are your two options.
528
:Um, and be like, "This
is, this isn't fair.
529
:I give up."
530
:yeah, it does suck, but giving
up's not a good option either.
531
:Giving up sucks too.
532
:So choose your hard.
533
:You either have the hard of making a good
resume and, and getting it in front of
534
:recruiters and hiring managers, or you
have the hard of you don't get a data
535
:job and you-- maybe you don't get a job.
536
:Both of those options are hard.
537
:It's just different hard
538
:Avery Smith's screen-2: Uh, also
there is a critical error that I did
539
:notice here is never ever do this.
540
:Uh, this, never ever do this right here.
541
:Um, I'm gonna give y'all a
second to figure out what's
542
:wrong with this, but this is
543
:Avery Smith-2: For our audio audience,
he is circling the dates for each
544
:one of the jobs in the professional
experience section, and they say twenty
545
:twenty-four to twenty twenty-five and
twenty twenty-four to twenty twenty-four.
546
:So he has no dates.
547
:He or she has no dates on their resume.
548
:Um, and sorry, no months.
549
:You need to have months on your
resume, um, because basically
550
:having no months can be a red flag
551
:Avery Smith's screen-2: that, in fact,
that entire thing I would remove.
552
:I wouldn't even put this on here.
553
:That's just gonna make you
look like a job hopper.
554
:Like, not even counting the fact
that your resume has nothing in it.
555
:Again, this is not the worst resume
I've seen in my life, but it's
556
:Avery Smith-2: This resume has
nothing in it, but it's not the
557
:worst resume he's seen in his life.
558
:So, uh, that feels like an
oxymoron sentence right there.
559
:Um, by the way, he's currently whiting
out this job that was from:
560
:because it makes this person look like a
job hopper or wasn't at the job very long.
561
:Also I'm assuming the, the companies they
work for, it says Fortune 500 automotive
562
:client and Fortune 500 utility provider.
563
:I'm assuming those actually have the
company names in the actual resume, um,
564
:because down below it says JPMorgan Chase.
565
:If not, that's-- I mean, you gotta
put the company you work for.
566
:You can't just say, "I worked
for a mystery company."
567
:Like that's not good.
568
:Like I don't know if this is how
he asks for, um, if, if he asks for
569
:resumes this way to be like a little
bit more protected and anonymized.
570
:I don't know.
571
:But, uh, I don't think that's great
572
:Avery Smith's screen-2: Not
even counting the fact that
573
:your resume has nothing in it.
574
:Again, this is not the worst resume I've
seen in my life, but it's very, very bad.
575
:Uh
576
:Avery Smith-2: It's, it's probably like a
four out of 10, maybe a three out of 10.
577
:It's not that bad.
578
:It's not that bad.
579
:Um, it's just wordy, no white
space, and yeah, poorly formatted
580
:Avery Smith's screen-2: Uh, and
then down here, again, that could
581
:be December twenty twenty-three
to January twenty twenty-four.
582
:I don't know.
583
:You need the months.
584
:This looks bad.
585
:Always, always, always.
586
:But that's all I can do for this resume
587
:Hopefully that gave you a good idea
of how you could improve your very own
588
:resume to start to get more interviews.
589
:If you want a blank slate and you want
a template that has been proven year
590
:after year, I'll have a link in the
description down below, or you can
591
:go to datacareerjumpstart.com/resume
592
:and download that for absolutely free
