
Some Reflections on Being Turned Down for a Lot of Data Science Jobs - emplynx
http://tdhopper.com/blog/2017/Mar/06/some-reflections-on-being-turned-down-for-a-lot-of-data-science-jobs/
======
SatvikBeri
I manage a data science team and revamped the hiring process pretty
substantially about a year ago, to good results. Nothing in here is
particularly original, but here's what we do:

1\. Break down "data science" into several different roles–in our case,
Analyst (business-oriented), Scientist (stats-heavy), Engineer (software-
heavy). Turns out that what we mostly want are Engineers-Analysts, so our
process screens heavily for those.

2\. Figure out which types of people can be trained to be good at those roles,
given the team's current skillset. I opted to look primarily for people with
strong analysis skills and some engineering.

3\. Design interview tasks/questions that screen for those abilities. In my
case, the main thing I did was make sure that the interviews depended very
little on pre-existing knowledge, and a lot on resourcefulness/creativity/etc.
E.g. the (2-hour) takehome is explicitly designed to be heavily googleable.

4\. Develop phone screens that are very good at filtering people quickly, so
that we don't waste candidates' time. By the time someone gets to an onsite
interview on our team there's something like a 50% chance they'll get an
offer.

On the candidate side, when I'm applying I try to figure out first and
foremost what a company means by "data scientist", usually by networking &
talking to someone who already works there. This filters out maybe 90% of jobs
with that title, and then I put more serious effort into the rest.

~~~
Declanomous
I've been looking for a job, and I've found how vaguely organizations define
their data scientist and analyst roles in their job postings really
frustrating. They tend to have a short description of the role, which is
generally filled with buzzwords, followed by a list of requirements. I wish
organizations would talk about what they wanted to do with their data instead.

For instance, a common description might say the candidate will be working
with "big data" to help with "data-driven initiatives" and the requirements
will be something like "knowledge of Excel, with a Masters in Statistics, or
equivalent experience".

It's really hard to tailor a cover-letter or a resume to a job posting like
that. For one thing, I can't even imagine what kind of work they are doing if
they are using Excel for "big data". Second of all, I currently have a job,
and writing cover letters and creating resumes takes a lot of time. By the
time I get to the phone screen I've probably already spent at least a couple
of hours applying. Plus, in the interest of keeping my cover letter and resume
short, I have to leave off a fair amount of my experience and performance
metrics.

Honestly, at this point I think I'm just going to start reaching out to people
in the fields I'm interested in and asking them if they know of any roles that
would fit my skill-set. The way I see it, I'd at least have a chance of
getting feedback from someone who can view my skill-set holistically, rather
than HR, who will let me know that I don't tick all their boxes (or vice
versa).

~~~
apohn
>>I've been looking for a job, and I've found how vaguely organizations define
their data scientist and analyst roles in their job postings really
frustrating.

I lead a Data Science team and part of the struggle with writing sensible job
descriptions is that there are too many people providing input into the job
description. HR can also put their hand in the pot when they try to use
buzzwords (e.g. Hadooop) to internally justify why a role with 2 years of
experience needs to be paid like other roles (e.g. traditional Excel based
Analyst) with 5-10 years of experience.

>>They tend to have a short description of the role, which is generally filled
with buzzwords, followed by a list of requirements. I wish organizations would
talk about what they wanted to do with their data instead.

One major challenge for Data Scientists is how hyped the role is, leading to
people in an organization believing whatever they want about Data Scientists.
Are you a leader who wants a business analyst who can use software and
interface with IT? Data Scientist. Are you an engineering manager who wants a
person who can interface with the business and use machine learning? Data
Scientist. Are you a VP who thinks big data and ML is the problem to your bad
or non-existent data? Data Scientist. Do you want somebody who can exhale the
maximum amount of hot air while still sounding like a tech and math genius?
Data Scientist.

Also add in that business people with minimal experience in modern Analytics
are trying to build up Data Science and Analytics capabilities in their own
part of the organization because they realize Excel is not the answer to every
question. I've spent a lot of time speaking with people to help them
understand the type of people they need to hire. Sometimes people are sensible
and sensible job descriptions and expectations come from that. Other times
they are adamant about what they need (even if they are wrong) and the end
result are convoluted job descriptions that are either never filled or filled
with the wrong person.

~~~
autokad
how do you choose candidates for an interview?

I can't even get a call back for an interview lined up. I have done NLP, got a
masters degree in computer science from Penn, plenty of experience with big
data such as hdfs and hive, spend my free time doing what ever data science I
can. but obviously doing something wrong.

any suggestions? Here is my linkedin account:
[https://www.linkedin.com/in/karl-
dailey-02557b65](https://www.linkedin.com/in/karl-dailey-02557b65)

~~~
shepardrtc
May I offer a couple of quick suggestions? I've never hired for Data Scientist
positions, but I've hired for plenty of other ones.

1) Change your profile picture to something serious. Get a collared shirt and
a nice background outdoors. No tie. People will unconsciously judge you on
your picture, so you want something that shows you're a professional, but
you're confident and happy in life.

2) Think hard about your job titles. Your latest job is far more than just a
"Data Analyst". It seems closer to a Scientist or Engineer role, even if your
company doesn't call it that. "Analyst" makes people think of entry-level
positions. Your DBA-Programmer position is more like a Software
Engineer/DBA/System Admin position. If you can't pick one, generally those
all-in-one positions can be known as Systems Engineers. Whatever you do, make
sure that DBA isn't the primary thing people see. In general, sell your
previous positions more. Like "IT". That's not a title, that's a department.

3) Expand on your projects section more.

4) Make sure your resume matches your LinkedIn. People absolutely look you up
on there. I did it all the time. When the two didn't match, I was suspicious.

Good luck.

------
kafkaesq
So you're at least occasionally getting actual _feedback_ from some of these
discussions. That's quite something, actually.

BTW, as to some of that "feedback":

 _Honestly, I think the way you communicated your thought process and results
was confusing for some people in the room._

"Okay, pal - let's put your people up in a room full of strangers (some of
whom show through their body language and/or constant phone-checking that they
pretty much don't really want to be there in the first place), make them
answer some made-up questions (which may or may not be coherently presented;
or even particularly relevant to the job description) -- combined with the
background stress of being unemployed, and/or stuck in job they absolutely
HATE, and can barely stand another day of -- and see how they do."

 _Quite honestly given your questions [about vacation policy] and the fact
that you are considering other options, [we] may not be the best choice for
you._

Great -- so they're basically accusing you of being a slacker (not really into
the work, only interested in what's in it for you, etc). Which is quite a
presumptuous attitude to take in response to a _perfectly reasonable question_
about the value proposition they're asking you to consider (a question for
which it might be in better form to wait until the later stags of the
negotiation process to bring up... but that's a very minor style point that
you definitely shouldn't be dinged for).

"Quite honestly" that attitude sucks. And you don't need to feel bad about
being "rejected" by people like that.

~~~
namelezz
> Honestly, I think the way you communicated your thought process and results
> was confusing for some people in the room.

Wow, get similar rejection lately: this is a fast paced company and the way
you explained your thought process was confusing and might slow us down, so
good luck with your job search.

~~~
daenney
Did you ask them what was confusing and how it would slow them down. Did they
tell you? Because ultimately if I'd hear that without anything to help me
improve it's a bit of a catch-all "we don't know so out you go" kind of thing.

------
minimaxir
Many recommend setting up an online portfolio for these types of positions.
But I've applied to a number of Data Analyst/Scientist jobs recently and I am
immediately rejected almost every time despite highlighting my blog/portfolio
([http://minimaxir.com](http://minimaxir.com)) and my GitHub with open-source
code/Notebooks for each and every post
([https://github.com/minimaxir](https://github.com/minimaxir)), both of which
have topped HN on occasion.

Internal recruiters have hinted that my Software QA Engineer background + no
CS degree implies I have no technical skill.

~~~
tom_b
I wonder if you are less impacted by the lack of CS degree than by your
"Software QA Engineer" label.

My own experience was that my initial position as a software performance
engineer resulted in a perception that I was a "tester" without technical
skills _despite_ having multiple CS credentials and published code in
practitioner-oriented sources.

Overcoming recruiter biases was such a struggle that I now routinely counsel
students and early career programmers to carefully consider whether job role
perceptions would negatively affect their future prospects. I also tell them,
when financially viable, to not take on roles where hiring orgs cannot give
them a day-to-day job description that directly matches their desired career
path.

This advice seems quite challenging or maybe misguided for data science
careers though. It seems like just getting an entry-level data science job
might require a dedicated MS in either CS or stats, with a healthy set of
projects in whichever of those two subjects you didn't spend grad school
working on to prove yourself . . .

~~~
yttrium
I had a similar experience. My first job out of college was for a Developer
Role (building testing frameworks, maintaining and building browser
extensions) but the job was titled 'QA Developer' so I had a hell of a time
the first time I tried to find a new job. Never mind that I wrote thousands
and thousands of lines of application code, lots of recruiters would deny me
on the basis that my background didn't fit.

~~~
santaclaus
Did you try dropping the exact title from your resume for a more general
description of the role?

~~~
yttrium
Yes, but it was hard to entirely obfuscate the fact that I designed/coded
testing frameworks. Part of my job was running and analyzing big batches of
regression scripts, so it would have been disingenuous to pretend like it
didn't happen.

------
wakkaflokka
I was coming from academia with no real competitive background in machine
learning/predictive analytics/statistics when I started applying for data
science positions. I was coming from a post doc in computational neuroscience,
and had done ML/your run-of-the-academic-mill stats as part of my thesis, but
never had formal training in it. I landed a lot of interviews (and applied for
an ENORMOUS amount of positions), but it took me a while to land a really
solid offer. So just keep at it.

Once I got that offer and 'data scientist' was listed as a position on my
resume, I've had at least one or two recruiters reach out to me each week. Not
just your 'bulk tech recruiter', but individual hiring managers/team leads
from companies who had no interest in me prior to the title change.

Hell, I had a manager I had already interviewed with from another company (and
got rejected) reach out to me TWICE to come back in. To be honest, I'm not
entirely sure he remembered that I had interviewed there only a month earlier.

All after I had the title change on my resume. What I'm getting at, is for me,
the title change really opened up doors. I figure 'data science' is such a
huge buzzword now, recruiters look more for that buzz word than they do for
the actual content of what you've done on your resume. I'm sure it will die
down at some point.

This may be more prominent outside of silicon valley where I am, and (in my
opinion without any facts to back it up), where I feel more weight is given
for the title than the substance.

~~~
aeorgnoieang
I still feel grateful for an otherwise-pretty-terrible employer for gifting me
with a 'developer' title. Regardless of how much experience or aptitude one
has or can demonstrate programming or building software, titles _are crucial_.

------
graphememes
I don't have much Data Science job hunting experience, however I have applied
to and been hunted by over 100 startups, 24 enterprise companies, and 12 large
"startups".

I've had the following:

    
    
      - 106 Rejections
      - 39 Offers
      - 24 Refused
      - 15 Contemplated
      - 5 Taken
    

Roles Applied for:

    
    
      - Software Engineer (55rj, 8o, 6r)
      - Product Manager (5rj, 6o, 5r)
      - Senior Software Engineer (31rj, 23o, 22r)
      - Product Lead (0rj, 1o, 1r)
      - Senior Product Manager (9rj, 3o, 2r)
      - VP of E (2rj, 1o, 1r)
      - CTO (0rj, 1o, 0r)
    

It's been a journey. I don't keep an exact list so this is from looking at my
calendar and doing some basic math. This certainly doesn't include all of the
role changes within companies.

Things to note:

    
    
      - People conflate Agile & Scrum a lot.
      - Make it easy for people in the room to know what you personally accomplished during your career.
      - Make an impression, don't overdo it however.
      - Make your descriptions easy to understand.
      - Don't just list tech stacks on your resume, list achievements.
      - Stay consistent.
      - Ask questions.
    

Also Note:

Regardless how impressive your Github & Stackoverflow accounts might be, you
will still be asked to do a stupid code challenge. It irks me. I understand
the reasoning for it when you don't have a viable pool of information on the
individual, but when you do... It's just disrespectful.

Few places I have been rejected by:

    
    
      Salesforce, Atlassian, Trello, Twitch, Segment, AirBnB, Twitter, OKCupid, LinkedIn, Shippo,  and many many more.
    

Some places I have been offered by:

    
    
      Amazon, VMWare, Oracle, Google (twice), Apple (twice), Venmo, Auth0, Discord, Youtube, Hitbox, Steam, Pusher, Cloudflare

~~~
michaelscott
This is a really interesting set of stats you have here. Heartening in the
sense that rejection is a common and overwhelming likelihood and shouldn't be
a source of dejection, but also kind of depressing in the sense that it's
clear many, many companies (including some big ones) have a terrible hiring
process.

~~~
graphememes
Yes, for example some take months with everyone loving you the entire way, for
a single person to interview you and reject you without a reason.

I'm glad I had those interviews early on, so now when I see an interview start
to drag on, I cut it short as it doesn't look like a priority on their end.

------
strictnein
> "Quite honestly given your questions [about vacation policy] and the fact
> that you are considering other options, [we] may not be the best choice for
> you."

I had a very similar experience. Job offer was basically on the table and then
they balked because I mentioned that I had another offer (at a larger company,
which they seemed shocked/annoyed with) and I had a question about parking at
their new offices. The current offices had free parking, so I had simply asked
if the new ones would too. The response was very cold (must have a been the
source of internal strife, I guess?). An hour later I got a call saying they
were no longer interested.

~~~
BickNowstrom
Mentioning that you had another offer can be tricky. Did they ask you if you
were seeing other companies? Did you just put it out there? Did you put them
on a deadline?

Mentioning that you have another offer is seen as a negotiating tactic. Then
they have to compete/bid against the other offer, and you are removing some of
their leverage/power. A strong reaction to such a move can be to cut off the
interview, so to regain back their power.

~~~
PakG1
Yes, you're correct. But it's best to walk away from companies who try to own
you to that level. If a good company respects and wants you, they are
completely OK with giving up some of their leverage in order to gain your
trust as well as they can.

------
motivic
This is a bit off-topic but one thing I'm curious is how the author manages to
interview with these companies while holding down a full-time job and keep up-
to-date with the latest developments. Interview for a data science position is
typically a drawn-out process, with multiple rounds of interviews and possibly
take-home projects. I found them to be very time and energy consuming.

To share my story, I also had a difficult time transitioning into a data
scientist role after leaving academia (pure mathematics), and I always thought
the root cause was my lack of experience and competency. So instead of keeping
on applying, I spent over a year just to sharpen up my skills. It paid off in
the end.

How can one develop his/her skills and cultivate expertise if one is job-
shopping all the time (possibly aimlessly)?

~~~
eanzenberg
I think being a good DS requires focused learning. For example, when I started
out, I didn't know much about neural networks and their different
architectures. I tend to find time outside work to go over papers, thesis, or
watch lectures on youtube to keep myself up to date so that now I can describe
cnn and rnns to tech and non-tech people.

------
bsg75
> Quite honestly given your questions [about vacation policy] and the fact
> that you are considering other options, [we] may not be the best choice for
> you.

Dodged a bullet on that one:

\- PTO is a touchy subject?

\- They are looking at more than one candidate, why would any candidate limit
themselves to one potential employer?

~~~
hudibras
Yeah, that's just weird. Paid time off is part of the total compensation
package; it would be strange if a candidate _didn 't_ ask about it.

~~~
cwilkes
And if they are touchy about it probably also means that they won't pay it out
if you leave ... that's the law in California but not in other states like
Washington.

I was shocked to hear that, apparently it is somewhat common in retail jobs
not to get paid out. Happened to me at a company that claimed to be more of a
technology company. Maybe they don't think word gets around. Or that smarter
employees will just take a lot of time off before quitting, which does a lot
more harm to the company than if they just paid out the time owed to the
employee.

------
preordained
The too many jobs knock I can understand...I'm just plain old Midwest
developer guy, so maybe I don't understand the nuances of a market like
Silicon Valley or the like that is inundated with software jobs. Still, when I
read HN I often get the impression that there is more focus on getting that
next great offer than actually doing a given job...so much so that the ability
to be perceived as valuable at interview/screening time seems like the mostly
highly focused on or developed skill for some people. It seems like there are
many professional interviewees out there these days.

I have a hard time understanding. At least for what I've seen/done, it would
take about a year of experience to be of any real value. I'd say you start
seeing real dividends from an employee near the three year mark. I think the
primary exception would be where you have a big gaping hole in an
organization...like building a data science program or something from the
ground up. But if you've got software and customers long long past the 1.0
stage to support, and someone is going to (someday) understand/contribute to
the core? Think about Google's monolith or the Linux kernel...sure most
software isn't that mature/grand, but there are many projects out there that
are closer to that than greenfield. And it's not just the code, it's the
developed relationships/rhythm with coworkers and customers.

Maybe I've explained it to myself, I don't know. The difference may be startup
companies or projects versus mature ones. At least in the latter case, it
seems to me that if a company retains an engineer for less than 2-3 years,
they've almost certainly lost on that investment.

~~~
emplynx
You may well be right.

------
alain94040
_You never really know_

That's actually the only correct answer. Having been on both sides of the
table for many years, I can pretty much guarantee that whatever reason the
candidate is given is nowhere close to the actual reasons.

There may not be any specific reason why we didn't pick you, but we'll give
you a tiny sample anyway. So you think that's _the_ reason - it's not.

In other cases, we have a strong reason not to pick you, but it's embarrassing
so we feed you a bogus reason instead.

And in more cases than I'd like, there is no problem on your side, we are
having internal issues that we can't reveal anyway. Any reason you hear from
us in that case is completely irrelevant.

By the way, when you evaluate people in interview, you really need to figure
out a _vector_ : where they are today in terms of knowledge and experience,
but also in what _direction_ they are going (fast learner or not, high
potential, etc.). Which is why sometimes you can hire someone who has less
relevant experience, but you think they'll learn fast and are very smart, and
sometimes you are looking for the perfect match to the current position, but
don't care too much whether they can pick up new stuff or not.

~~~
bbarn
HR Departments have also groomed hiring managers not to ever give a reason why
they say no. Every reason you provide is grounds for speculation and potential
law suits.

------
CSDude
At least you have been told the reasons, even if they are true or not. I
recently had many tech/behavioral interview with team and passed, but after a
review with VP of X, saying we decided not to move forward is way worse than
this. I still keeep wondering 'What is wrong with me?' even after years of
interviews. I have some suspicions, but never a promising answer. If I oneday
found a company, first company policy would be to tell the candidates to tell
why they were not hired in a polite way.

~~~
jedberg
> first company policy would be to tell the candidates to tell why they were
> not hired in a polite way.

And your lawyers would _strongly_ advocate against that.

In most cases they can't/won't tell you because it could open up legal
liability. If they say a reason for you, but then hire someone else to whom
that reason also applies and you find out, you could sue them. Or you could
find a way to twist it into something against a protected class. Too many ways
for the company to get screwed.

That's why they all say "we've decided to go another way" or something equally
generic.

~~~
yuhong
This is part of why I dislike employment anti-discrimination laws. They
probably worked better for manual labor jobs. Of course, there is also firing
where the laws vary by state. I think CA is one of the most strict, right?

------
pascalxus
The real reason you were rejected, or anyone applying in a highly competitive
field: an oversupply of qualified candidates.

~~~
ska
In this particular case (data science) it is more an oversupply of candidates
(qualified and not), plus difficulties defining and measuring "qualified",
plus buzz.

It's a difficult enough field to hire in when you understand what it is (and
isn't) - and lots of companies are trying to do it with far more vague goals.

~~~
throw_away_777
Why do you think candidates for data science jobs aren't qualified relative to
other positions?

~~~
yummyfajitas
I've probably interviewed about 70-100 such people in the past year and a
half. Exactly 1 such person was qualified (I hired him). The issue in my view
is the following: people who know both statistics and computer science are
extremely rare.

People who actually understand statistics are rare. I can probably weed out
1/3 to 1/2 of candidates simply by asking what a p-value is, or what
precision/recall are (this includes people who said they worked in search).

Of the ones who know basic stats, most are neither good at nor interested in
programming. They just want to use existing libraries to crunch numbers in a
Jupyter notebook, then hand that off to the developers.

Finding a person who can come up with a predictive model, understand what they
did, optimize it without breaking it's statistical validity and deploy it to
production is very hard.

(If you can do this, I'm hiring in Pune and Delhi. Email in my profile.)

~~~
halflings
Ignoring what a p-value is does not mean that you don't know statistics.
p-tests are not some inherent statistical property, they're just a useful
model for significance. People coming from a CS background most likely didn't
have to deal with p-values, but they can still be good at linear algebra or
bayesian statistics.

(not sure I can defend somebody that does not know what precision/recall are)

~~~
Bootvis
Regarding precision/recall, I've a background in financial econometrics and
this is the first time I encounter the terms.

~~~
jknoepfler
It's literally the first thing you learn in data science / machine learning
coursework about evaluating model performance. It would probably be better to
ask the candidate to whiteboard a set of metrics for evaluating model
performance rather than ask for the definition of a pair of words, but the
concept is practically the for-loop of data science.

Edit: note that I'm not saying you need this to add roi as an analyst for a
business!

~~~
Bootvis
I haven't taken a lot of data science classes but I'm not sure that's true. If
you start with linear regression the mean squared error would make more sense.
I actually searched through "The Elements of Statistical Learning" and the
word 'recall' is not used in this sense at all.

~~~
mehaveaccount
People don't pay for linear regressions. They pay for discrete things: what is
my best option among my three clear courses of action. Linear regression can
be a tiny piece of a larger argument in favor or against one option or the
other, but that alone doesn't make money.

~~~
Bootvis
That's obvious but not at all what I responded to in my post.

I responded to the claim that ML courses start with the definition of
precision and recall. In my admittedly limited experience those courses start
with linear regression and mean squared errors. After that, there is so much
generalization possible and that doesn't include precision/recall.

You make money by solving someone's problems, making money by stating
definitions is only done on TV quizzes.

------
CalChris
_Companies often use interviews as a time to figure out what they 're really
looking for._

For startups, this transcends data science. It might be the one time that week
they focus on that need.

 _Networking is still king._

Exactly and this also argues against wanting to get hired to work remotely.

~~~
emplynx
Not sure networking is contrary to working remotely. Opportunities for
networking online are far bigger than in-person.

~~~
CalChris
You may be talking about contracting and the OP is talking about hiring.
Moreover while the opportunities for networking online are countably infinite
they can be thin opportunities. I know Joe from HN doesn't carry the same
weight as I know Roger from Google. It carries weight just not nearly as much.

------
eanzenberg
My analytical thought process on DS interviews:

\- Signal is still quite low among noise, even with long multiple interviews,
take-home homework, coding challenges, etc. Most relevant data is still hidden
and takes months-years to come out.

\- Companies seek to minimize false-positives much more than minimizing true-
negatives.

\- It's a numbers game from both ends because the probabilities are low, due
to above 2 points.

~~~
FireBeyond
Woe betide the company that asks for a take home project, especially one that
isn't just 50 lines of an algorithm (I'm talking one that I recall that asked
for parsing Apache logs from a stream, displaying moving averages for URLs,
aggregates, having high water mark "alerts" and resetting when rolling
averages dropped below that, some unit tests and docs...

And doesn't bother to respond after you submit. I'm certainly not the world's
best coder, but my solution met all those requirements in a reasonably elegant
manner, and took several hours (10-20?) to make comprehensive.

That gets you on my shit list, and people I know get to hear about it, too.

~~~
eanzenberg
Same FireBeyond. Even worse, I've gotten feedback on a take-home project where
the solutions had errors in it and I had the correct response! This, btw, was
from one of the huge tech-companies in SF. Since it was the recruiter who went
through the solutions, I couldn't nor cared to correct their mistakes :)

------
donovanm
Is there a typical path for getting into Data Science? Posts like this make it
seem like it's the wild west

~~~
dbecker
I've had a few DS jobs. And I've been a hiring manager at two of those jobs. I
can confirm that it's roughly the wild west still.

------
autokad
I can't even get a call back for an interview lined up. After hearing people
get tons of unsolicited mails on linkedin from recruiters (while I got none),
I tried adding more words in my linkedin to show up on searches, but still
nothing.

Does anyone have any suggestions? Here is my linkedin account:
[https://www.linkedin.com/in/karl-
dailey-02557b65](https://www.linkedin.com/in/karl-dailey-02557b65)

------
rubayeet
"Networking is still king"

My takeaway from it.

~~~
wgyn
I think that just "networking" would be the wrong takeaway, though. The
author[0] is pretty active on Twitter / the blogosphere and, even though I
don't follow him personally, his writing has popped up on my feed a number of
times. He's likely meeting people and having interesting conversations
("networking") because he has interesting things to say.

[0] [https://twitter.com/tdhopper](https://twitter.com/tdhopper)

~~~
mindcrime
Not just blogging, but Tim is also active in terms of attending / speaking at
various industry conferences and what-not. Whether he is doing it simply to
"build his brand" or because he genuinely enjoys it, or both, is something I
can't speak to. But I think he's definitely "meeting people and having
interesting conversations".

~~~
emplynx
Hi Phil!

~~~
mindcrime
Oh no, I've been outed!

------
ENGNR
Clicked hoping he was going to do data science on his failed interview data

------
segmondy
I looked at this resume. it looks good but his job history is a major concern.
Of course, some might say why even invite him to an interview, perhaps they
gave him a chance only to discover in the interview process that he's really
impatient and not willing to stick around for long?

There is a ramp up time for new hire, which could be a couple of months. So
durations of a year or less doesn't look good. I personally like to see
minimum of 2 years for each job. Of course, too long can be a concern too
unless they really grew in their role.

I agree with his conclusion, Network is king, but I also believe in listening
to the universe. If everyone is "wrong", maybe you are the problem. If lots of
people don't wish to hire you after you have solid experience in the industry,
something is wrong with you and you are being stubborn by refusing to
recognize it and fix it.

------
mattfrommars
Ok, this is a little worrying. I've read through the all the conversation here
but is it really that difficult to get jobs for people who already are
involved in Data Science? Here I was thinking if I do a course on Data Science
fundamentals of computer science online, I would on tract of getting a
computer science/data science job.

My future really rests upon this. I have a degree in mechanical engineering
with irrelevant experience. My only regret is not doing bachelors in computer
science.

I wouldn't lie if all the post got me little tensed and made me thinking,
"what am I doing with my time". About to finish introduction to Computer
Science using Python on Edx.

------
carterehsmith
That sucks.

That said, what is this?

a) "I cannot do X" b) "But here is some advice on how to do X"

I mean... why would anyone listen to the advice that is proven to lead to
failure? Serious question.

------
LeanderK
Well, it also seems like he applied to a lot of jobs (he worked for 5
companies since 2012, for many only under a year!). He must be constantly
looking for a new job.

I know switching jobs is common here, but i would think that sticking at least
1 to 2 for a job would be normal (assuming it works out).

------
megablast
> you rarely truly know why you were turned down.

Bit of a silly article. You could say this about anything. And I don't see
what the problem is with never being contacted, if someone doesn't want me for
whatever reason, I don't really want to hear from them again.

~~~
emplynx
K

------
anotheryou
While I find it reasonable, I would still not ask about vecation policies in
the interview, but rather when they decided they want me and we go through the
details. Why would you ask before? (honest question)

~~~
emplynx
I actually had an offer in hand, and it was rescinded after I asked about PTO.

~~~
anotheryou
Than it's good you weeded that one out :)

------
edblarney
"Quite honestly given your questions [about vacation policy] and the fact that
you are considering other options, [we] may not be the best choice for you."

You wouldn't want to work there.

" but the team has decided to keep looking for someone who might have more
direct neural net experience."

Fair enough - but this has to be slim pickins. How many AI jobs are there out
there? Realistically, very few.

------
draw_down
> "Honestly, I think the way you communicated your thought process and results
> was confusing for some people in the room."

We got confused, and then we stopped. The end.

------
gillianlish
ironically, the unwashed masses that data science has 'targeted' for
'optimization' go through the exact same process and feel the exact same
emotions.

apply for 100 jobs, get zero response, and zero reason why.

be told illogical things.

be told out right lies.

welcome to capitalism. welcome to the workplace that is run entirely by data.
(or by people who only care about data). welcome to the future of humanity.

