Latest / Agentic Conversations (formally mlops.community) / MLOps meetup #13 // Maximizing Job Opportunities as a Data Scientist on the Market With Anthony Kelly

Transcript

MLOps meetup #13 // Maximizing Job Opportunities as a Data Scientist on the Market With Anthony Kelly · Agentic Conversations (formally mlops.community)

• 0:00
  So before we get started I will use this moment to plug a few
• 0:07
  things and I just want to let everybody know that if you're
• 0:11
  not in it already we have a ml Ops slack community group.
• 0:17
  That is open to everything ml Ops.
• 0:20
  So if you're coming and you want to check out more about machine
• 0:24
  learning operations or devops for ML as they call it, feel
• 0:28
  free to jump in that I We'll share the link in the chat in a
• 0:32
  minute. I'm going to have to go find it
• 0:34
  real fast but and then there's also a machine learning
• 0:39
  operations or ml Ops conference that somebody in the community
• 0:44
  has reached out and they are part of the community and
• 0:48
  they're putting on this Workshop or this conference.
• 0:50
  They said that they would give anybody that's part of the
• 0:54
  community a discount on the ticket prices.
• 0:58
  So if anybody's interested in I also put the link in the image
• 1:04
  at and I think that's about it. Let's go ahead and let's get
• 1:09
  started here. We've got a lot to cover and I
• 1:13
  want to make sure we get to it all.
• 1:15
  So our guest today on the show on our weekly meet up, which is
• 1:23
  this is a very unique, weekly meet up and I reached out to you
• 1:28
  Anthony about A month ago and I love Richard doing and I thought
• 1:33
  it would be very valuable to offer people some kind of
• 1:36
  Workshop looking at how they can better their CVS and resumes and
• 1:41
  you came right back at it with. Yeah I got a lot of material on
• 1:45
  that. Let's do it.
• 1:46
  Let's go for it. So for me it's an absolute
• 1:49
  pleasure to have here. Just so everybody knows Anthony
• 1:53
  has been doing this people sourcing gig since 2015.
• 2:00
  He's originally from Ireland, now living in Berlin, which is
• 2:05
  one of my favorite cities in Europe.
• 2:06
  So I understand why you're there and what took you there, there's
• 2:10
  the company he works for is also in New York City, so he's
• 2:14
  working out of Ireland Berlin. New York City.
• 2:18
  You, you also did something interesting, Anthony, which is
• 2:21
  you started your own meet up the AI and action meet up in Berlin,
• 2:26
  which I really think is a great initiative and your Leading the
• 2:30
  AI in action podcast, so you're no stranger to this format.
• 2:36
  I would love if everyone gives a more.
• 2:39
  Welcome to our guests. Anthony Kelly, thanks for coming
• 2:43
  on here. Thanks for being with us.
• 2:44
  Anthony, how are you doing today?
• 2:47
  I'm good to me. Tales, thank you.
• 2:50
  I'm really, really nice introduction there.
• 2:53
  As you're right, I'm no stranger to hearing the sound of my own
• 2:57
  voice and Going and doing the introductions up.
• 3:01
  My, you know, still cast all called Ai and action.
• 3:06
  For those who are interested, they also cover the areas of
• 3:11
  data science and data engineering data analytics,
• 3:16
  business intelligence. We call it.
• 3:17
  It's like the AI ecosystem and I've been doing recruitment for
• 3:22
  five years now started off with no experience to become the top
• 3:28
  Banner of a A company that was from the UK to become the number
• 3:33
  one recruiter in that company across the UK and Ireland, in
• 3:37
  less than three years time. So, I achieve quite well.
• 3:42
  And I'm looking what? I feel always differentiate me
• 3:45
  from other Growers is I always help my candidates, give the
• 3:49
  best interviews or prepare the CV to be bad to get them those
• 3:54
  extra interviews. So what am I to you lookin to do
• 3:58
  a lot of looking to provide, Is I say I think choice.
• 4:02
  I want to I want to make every person that works for me have
• 4:07
  Choice after America. I don't want to ideally I can't
• 4:11
  have you have a multiple opportunities whether all with
• 4:13
  me or whether it away your own sources as well.
• 4:15
  That's absolutely fine but the end of the day, I'm happy if you
• 4:19
  have choice and we do that by getting you to the final stage
• 4:23
  as many interviews as possible to get saving as many offers to
• 4:26
  getting you as many interviews as possible.
• 4:30
  So I've got two different slide shows.
• 4:34
  One on preparing a CV, which is more to short one.
• 4:38
  And then the second one, I'm preparing for an interview.
• 4:42
  Awesome. So so before we jump into that,
• 4:48
  I wanted to ask you a few questions if you would allow me
• 4:51
  and I see there's a chat question in the chat to from
• 4:55
  William. We can get into that in a
• 4:58
  minute, William. But I just want to know what got
• 5:03
  you into the recruitment space and what drove you into this
• 5:07
  machine, learning data, science, data, engineering space.
• 5:12
  Sojat it's quite it's quite funny.
• 5:14
  I ended up in Recruitment and everyone will tell you to stand
• 5:16
  up in recruitment by mistake ahem I was actually working as a
• 5:22
  team lead and Itali sales call center.
• 5:24
  And I was, I was working these incredibly long hours at work, a
• 5:28
  50, 60, 70 hours. Weak managing a team of 80
• 5:32
  people in Dublin and I think of, 250 Kelly sales agents of the
• 5:36
  Philippines. So, I was the first person in
• 5:38
  and the Dublin office and leave and when the team was leaving in
• 5:42
  the Philippines, five days. We was all.
• 5:45
  I knew, I don't have a strong education background, so I would
• 5:51
  say gray and ambition kind of got me into into recruitment
• 5:56
  because a lot of people join Recruitment and Tend to leave
• 5:59
  very quickly because it's, it's not it's not an easy job for
• 6:03
  Polar form. Quite quite well, very early,
• 6:06
  very fast. Someone connected to me on
• 6:10
  LinkedIn and inherit Fork river. That was my first-ever
• 6:13
  engagement with a recruiter. And then I, without I joined the
• 6:16
  company called computer Futures, and and quickly learned the
• 6:21
  ropes and had to sort of its kind of like a sink or swim
• 6:26
  situation when you join recruitment.
• 6:28
  That's, that's how I got into it.
• 6:30
  Alright. And so how does, how have you
• 6:33
  seen the job market now since covid, hit?
• 6:36
  What is it? Looking like in your opinion and
• 6:39
  especially when relating to machine learning, jobs and data
• 6:43
  science jobs, you do with. So I've all markets, it is one
• 6:49
  of the most promising but still not as promising as it was four
• 6:53
  months ago and being being very honest and Boys, I've seen a lot
• 7:01
  of seen a paper. I will internally.
• 7:02
  We made a bit of a fever. If you're sort of looking at
• 7:07
  areas that are going to stay prominent to stay healthy.
• 7:10
  Health Tech fintech. Areas of innovation is what we
• 7:14
  call them. So, areas of NLP computer
• 7:16
  vision. So we've picked these areas
• 7:19
  because fintech and health Tech will be least and Will be least
• 7:26
  effective by this and then the areas of innovation will still
• 7:31
  have targets to me for their investors.
• 7:34
  Will still have product delivery goes to me, and I will find that
• 7:37
  those to be around a lot of areas such as again, LP and
• 7:40
  computer vision, and a lot of it, you know, especially being
• 7:45
  especially Medical Imaging is becoming quite popular now,
• 7:48
  particularly in Berlin and I noticed some really good
• 7:51
  companies, as well in the US, the other two, There's that
• 7:55
  there's a lot that's a lot going on in these areas.
• 7:57
  Okay, just to give us an idea. Before we jump into this, you
• 8:02
  rough guesstimate of how many resumes and Linkedin Pages.
• 8:06
  You've looked at over these five years that you've been doing
• 8:08
  this. Well, if we say I'm connected
• 8:12
  with 25,000 people, I think then I have a lot of people you
• 8:17
  connect with also, don't accept you as a recruiter.
• 8:21
  So LinkedIn pages, I would say, easily over a hundred thousand
• 8:27
  resumes. I'd said nearly see, at least 50
• 8:30
  resumes day, if you try and put that out over here, you know,
• 8:36
  you're probably talking in the tens of thousands.
• 8:38
  Well, yeah, okay, so you know, a good resume from the bad one so
• 8:43
  that gives us a lot of confidence when we're taking
• 8:47
  your advice on this and So yeah, let's go ahead and get into it.
• 8:51
  I'll without further Ado, go ahead and and let us know what
• 8:55
  you got, what you have prepared for us.
• 8:58
  And if anybody has any questions or needs, anything, feel free to
• 9:03
  ask it in the chat and we'll try and get to that.
• 9:06
  Oh so I did say on this slides its top tips for I need a
• 9:12
  professional. See contributor CV my initial
• 9:16
  background before I was recruiting for data, I was
• 9:18
  actually recruiting for a test Automation and devops.
• 9:22
  And basically on my very first day of all this, I took my notes
• 9:26
  from my last job, which was recruiting for Q&A and test
• 9:28
  automation. And brought it over because I
• 9:32
  find, it's the very same, no matter, no matter.
• 9:35
  Where you go just want to make sure there's something in the
• 9:38
  Trap. My my quality is kind of like an
• 9:41
  have a look, just give me two seconds.
• 9:48
  Yeah, I think I hear you are right.
• 9:50
  Can you you all hear me? Okay.
• 9:56
  I think we're good. We got it.
• 9:58
  We got what. We got people.
• 9:59
  I'm sorry if it's not the best, but it's peek at that fine.
• 10:09
  I'll just, I'll just speak a little bit louder, and yeah.
• 10:12
  So what I did is I actually took all the work that I've done
• 10:15
  previously in my other company on how to build a good CV for
• 10:20
  devops or QA test Automation and I replicate data from that.
• 10:25
  That to a data professional. And it's, I look for the same
• 10:29
  things all the time and the history of me, finding this
• 10:32
  information is. I see seavey's all the time and
• 10:38
  where the difference is with it. With a recruiter, my opinion is
• 10:41
  a good recruiter will always ask why aren't you interviewing that
• 10:43
  CV? And why are you interviewing
• 10:46
  that CV as? So that's kind of what I've what
• 10:48
  I've built this song. That's great to me.
• 10:54
  So I would always say, you know, used to my use the right
• 10:57
  headings to grab a managers attention.
• 11:00
  And so CV is never one size fits all so I kind of break it down
• 11:04
  into following sections. He can lay out like this when
• 11:11
  people are doing their experience.
• 11:13
  This is this is to me is the most important part.
• 11:18
  I see a lot of times company name job title time.
• 11:23
  I was there three bullet points. That is a personal pay of mine.
• 11:28
  I've seen people who've worked in companies for 45 years and
• 11:33
  it's three bullet points. It's just it just doesn't give
• 11:37
  enough depth about what you're doing in that role.
• 11:42
  But I would always start with the most recent relevant
• 11:44
  experience. So your job title, your company
• 11:47
  name. I would always put in as well
• 11:50
  one to two sentences about that company because there's a
• 11:53
  chance, that, that company who you're sending your resume to
• 11:56
  has never heard of them, or if you're relocating to another
• 11:59
  city, You okay, if I tried to tell you about turning think
• 12:04
  company in Ireland Global, our Global Force like, oh, yeah,
• 12:08
  what can Global for us? Here's my company.
• 12:10
  I'm a software developer, nobody's going to know who
• 12:13
  Global for side. They're not gonna know.
• 12:14
  So they're HR software Patron, award software.
• 12:18
  Nobody's going to know that, but you could be applying this foot.
• 12:21
  You could be applying to HR staffing company, or a company
• 12:23
  that has HR software and they're just going to Breeze over at the
• 12:26
  never going to consider you I am what we want to do is you want
• 12:31
  to include your duties responsibilities but then you
• 12:35
  also want to include your accomplishments.
• 12:37
  So if you worked on building a data pipeline or, you know,
• 12:40
  worked on a recommender system that was already in play and
• 12:45
  you, you know, made some changes to it.
• 12:47
  You also want to add what that meant to the business.
• 12:50
  So that means your search ability increased Comfort, your
• 12:53
  SEO improve by X percent and then if so, and if you have
• 12:57
  these figures, you know what Value that bring back to the
• 12:59
  business. So for us you know we see a
• 13:01
  Surefire CV is short for interview on the CV.
• 13:06
  Is when someone can say I work with this, I built this product.
• 13:09
  This product was built to serve this problem that the business
• 13:12
  had and this is the overall result.
• 13:15
  When we built this product, it provided X for the business.
• 13:19
  That's, that's where that came in.
• 13:21
  And if you can do that as much as possible within your CV, and
• 13:24
  on the, I think it's really helpful.
• 13:27
  Also, when you're talking Well, Technologies, you want to talk
• 13:30
  about like the depth of experience that you have with
• 13:34
  each technology? Yeah, I think that's that's a
• 13:37
  great point that we mentioned before is how you were talking
• 13:40
  about. You know, this can you just
• 13:42
  reiterate what we said before? Everybody jumped on about the
• 13:46
  idea of, if you're working with python and somebody is looking
• 13:50
  for a Java guy or girl, how can that help?
• 13:54
  Can you just talk about that again?
• 13:58
  Yeah, so I suppose I follow the same.
• 14:01
  Ethos that I would have been mentioned to me, a good
• 14:03
  programmer is always a good programmer, and but if you have
• 14:06
  worked in the same company for six years, and it's a point and
• 14:09
  programming experience for six years.
• 14:11
  But in this company, they don't know if you're an admin or if if
• 14:15
  you're not programming with it at all.
• 14:18
  But even if you're a point in going or applying for a job role
• 14:21
  or whatever language, it might be, it's quite relevant.
• 14:24
  I actually, if you have a well-written CV because you can
• 14:27
  explain While you're a good developer and why they should be
• 14:30
  meet you anyway, so you want to explain them how you're building
• 14:32
  features at the depth of your knowledge.
• 14:35
  Are you interviewing? If you're working with building
• 14:37
  data pipelines, are you integrating these pipelines with
• 14:39
  each other? You then, integrating this with,
• 14:41
  you know, build tools with AWS are you, you know, there's so
• 14:46
  much you can include, but someone might just have, you
• 14:49
  know, worked on by worked on building data pipelines, maybe
• 14:53
  two or three, bullet points, and then tools and Technologies, and
• 14:56
  it's just keyword, smash. Which is very frustrating to
• 15:01
  see. Perfect.
• 15:06
  Yeah, that was, that was exactly what I was thinking about.
• 15:10
  So it's stuff like this, this is what I mentioned her on the
• 15:15
  adding value. So you can see this one article
• 15:19
  sales, forecasting model, 25% Improvement.
• 15:22
  Overall production systems business impact is expected to
• 15:26
  be between 50 to 100 million per year.
• 15:28
  That's an example of an a/b testing, but that's, you can use
• 15:31
  that for anything. It can be for data science, it
• 15:34
  could be for machine learning. It could be for data,
• 15:36
  engineering analytics. I've seen people who can do a
• 15:39
  lot of it and analytics as well. And I think that's that's going
• 15:43
  to be a big difference. That's what.
• 15:45
  Stop someone from from interview, new versus just
• 15:49
  turning your CV to the next page.
• 15:53
  Education. So again you want to go
• 15:56
  education at the top Then followed by your experience.
• 16:03
  So sorry. But then again if you're a
• 16:05
  recent graduate, your education is the Highlight.
• 16:07
  Obviously, that's that. That's what you want to that you
• 16:11
  want. That's what you want to do.
• 16:13
  And so remember to only post your secondary degree courses,
• 16:18
  and I go into greater detail of this, go into great details of
• 16:22
  your top. Topics go into one thing that I
• 16:25
  actually don't even see a lot on graduate intern seavey's.
• 16:29
  Is that they they don't explain the challenges that they set out
• 16:32
  to solve and they talk about what they build but they don't
• 16:35
  talk about the challenges. See, these are also a way of
• 16:39
  addressing challenges. I would always say right to see.
• 16:41
  They have you down for an interview question and so, I
• 16:45
  would recommend that in the same.
• 16:46
  Sorry, I'll come back to that question.
• 16:48
  Now, on a second, just when I get to the end of this, And then
• 16:52
  you also want to go eat your latest experience at the top go,
• 16:56
  all your latest education at the top going down to you know,
• 17:01
  bachelor just for example, on screen there.
• 17:07
  Sorry, test. A lot of features that have been
• 17:10
  added to this. So when I hit next, it actually
• 17:13
  on the lines, they head off it and just so everybody's clear
• 17:18
  because I imagine some people are taking screenshots right now
• 17:21
  trying to get this. Can we share this with them
• 17:24
  afterwards? You can share this.
• 17:27
  I also have a dish or a one-page pdf version that I can share
• 17:31
  with you that you can send though that's true, but they
• 17:33
  did. This is a lot more detail.
• 17:35
  When I when I'm talking to a candidate, I would send them to
• 17:38
  PDF and I said I'm going to bring you to talk about this PDF
• 17:41
  and this is what I speak to him about cool.
• 17:44
  So this is the scales part. So, this is where I was talking
• 17:46
  about, you know what you're saying.
• 17:50
  And Don't kill your CV with skills.
• 17:56
  I like this example that I have here if they decide this for two
• 17:59
  years of experience but list six programming languages on their
• 18:03
  CV raises red flags, people look for Specialists data science
• 18:08
  specialist and or someone who's got a specialism you know you
• 18:13
  don't want to go in and try and be the Jack of all trades and
• 18:15
  master of none. Right?
• 18:17
  Watch or really go da on the CV and highlight them and write
• 18:22
  about them. Well, Give an example of where
• 18:25
  you implement this. So you've got so far I guess
• 18:30
  this is that a little bit, a little bit different, we've got
• 18:33
  core skills so you can you can write your core skills like this
• 18:37
  and or you can write them like computer skills, but I would not
• 18:41
  rely on these to talk about your skills.
• 18:45
  So in some cases, I actually find seavey's to be bearing.
• 18:49
  Without these I've got so much stuff here.
• 18:54
  This can all see this. So, I would like to write them
• 19:00
  as project as opposed to scales, so you're always focused on
• 19:04
  solving a business problem. You're always focus on solving a
• 19:10
  business problem. And I've actually included a
• 19:12
  example, at the end of this watch, I just want to show you
• 19:15
  how to explain it, but it's very much.
• 19:16
  The same thing you want to talk about the end to end of what
• 19:20
  you've solved. Whereas, someone just like, you
• 19:21
  know, providing programming experience worked on our
• 19:23
  recommendation system, whereas you get a, I see some great TVs
• 19:28
  where someone might turn around, they might say, I was part of
• 19:31
  Eckstein on this team, we were responsible for improving the
• 19:34
  recommendation system. That was performing at X
• 19:37
  percent. With collaboration with like the
• 19:40
  analytics team, we felt that we could perform or increase the
• 19:44
  return on investment of the rest of the value to this.
• 19:47
  So, what I've done is, you know, if you re engineering data
• 19:49
  pipeline, if you introduce a new technology, what you do, how you
• 19:53
  integrate this into the business, how you deploy this,
• 19:56
  and then you're talking about all the values, all the new
• 19:59
  technologies, and how you're integrating them and making all
• 20:02
  these pieces, talk to each other.
• 20:04
  And then you say, as a result, this solve the Challenge we've
• 20:08
  set out at the start you know. Maybe it was improved yeseo by
• 20:11
  5%, in fact we increased by 8%. This is the value and then if
• 20:18
  you've got any Publications I would include them more.
• 20:21
  So towards the end because they are very, they are great to see
• 20:25
  and if you can boy, if you can get some of the boy into your
• 20:28
  CV, they'll read the whole. See me, I am but a lot of the
• 20:32
  times less you're going for a very research, heavy roll
• 20:37
  locations Will not get to draw the draw, their full attention
• 20:41
  in early and is this true even with a blog post again?
• 20:46
  Yeah, I would say included, included more so towards the
• 20:49
  end, unless you have, you're applying to a referral to a
• 20:54
  friend where you know that a certain person is engaging to
• 20:58
  your content and you kind of want to lead with that.
• 21:02
  But I suppose as a As it as a, as an easier, one or more
• 21:08
  direct. I think you know, if you can do
• 21:11
  your your experience writing talk about that, that's that's
• 21:15
  where the real value is. And then they're going to look
• 21:18
  read the rest of the CV. If they see someone that's, you
• 21:22
  know, talking about publications, there just go up
• 21:24
  to research focused. We need someone that's more
• 21:26
  industry at the moment, which even if you've got that mix
• 21:30
  between industry and research you stood, they still Need to
• 21:34
  see the industry first. Unfortunately, it's just it's
• 21:36
  just how the feedback goes and crafting the perfect kovala.
• 21:43
  This is what I would do if you're depending on the level,
• 21:47
  you're at. If you're very mature, plug, you
• 21:51
  know, the management level to the director style level, I
• 21:54
  would say, individually create a cover letter for each individual
• 21:58
  position. Interested to the manager, it
• 22:01
  rested, and Roy. And on the show that you've
• 22:03
  understood the company, you actually read what they're doing
• 22:06
  a dress, that what you feel challenges that they're facing
• 22:09
  based on your findings, from looking at that company, and how
• 22:12
  you would do that? That that's what I would say.
• 22:16
  And this is to go for people where, you know, the might only
• 22:19
  be six companies where you could actually get a job.
• 22:22
  Or if you're super Niche, I want you to do this.
• 22:25
  Is this is the approach I would use.
• 22:27
  I was actually I've seen those people out there who are like
• 22:31
  career advisors, who charge people like 15 other Euros to
• 22:34
  tell them. You should write an accursed
• 22:36
  analyze cover letter for each company and That's at that.
• 22:43
  You apply for yeah they're making out real smart so I have
• 22:49
  an example one here, you know your data science, my CV with
• 22:54
  the detailed experiences attached and this is where I was
• 22:59
  you know most cases try and work the system a little bit you
• 23:03
  know. Engaged managers directly don't
• 23:07
  Don't go to this to the inbox. I'm a recruiter.
• 23:10
  I've been I've not been able to knock out one person's email
• 23:14
  address you guys attack guys use node as websites out there that
• 23:17
  will give you someone's work email and then you just have to
• 23:20
  get the prefix address it to them and say, hey look, I wanted
• 23:24
  to message you directly because I like what you do, read the
• 23:28
  LinkedIn, see what they're doing and then you're making that very
• 23:31
  relatable to them. But again, this is something
• 23:33
  that is for, if you're chasing a or if you have Dream company
• 23:37
  that you want to work for. This is how you approach that
• 23:40
  one company. You know, if you have you know
• 23:43
  your your white whale to say this is this is what I'd
• 23:47
  recommend. And so just I want to pause you
• 23:50
  real fast and try and get to these questions here.
• 23:54
  I see Antonio had one in the chat was asking about if you he
• 23:59
  wants to change his career from software development to data
• 24:04
  science. Do you have any recommendations
• 24:06
  on how to go? Oh about that.
• 24:08
  Yes. So definitely I would your
• 24:12
  resume should follow the format. As I've mentioned, you want to
• 24:16
  be talking about the Project's, how you're adding value.
• 24:20
  I do think it's it's one of the more difficult approaches is to
• 24:23
  go from software engineering to data science may be from
• 24:27
  software engineering to machine learning or maybe come into a
• 24:29
  data engineering position and first pick up that sort of that
• 24:34
  ecosystem experience that Data experience.
• 24:39
  You see a lot of data Engineers moving into m l.
• 24:42
  Now, your software skills will be very much appreciated.
• 24:46
  They are very much needed in these, in these in these data
• 24:49
  teams. You know, they lack in most
• 24:52
  cases, not saying they're all cases.
• 24:54
  But in most cases they do like certain that experience with
• 24:58
  supposed to not data scientist and talk to speed up things or
• 25:02
  to do them to make them run more efficiently to shave off time
• 25:05
  off runtimes. And solid principles isn't so
• 25:10
  much in built into their DNA because it's, you know, they
• 25:13
  might come from a statistics or a Max background or as you know
• 25:15
  if you study computer science. This is this is more your game.
• 25:19
  So you kind of want to still write a very strong CV and talk
• 25:23
  about how you interact or how you engage with data where yeah,
• 25:27
  comes into your team and might be something that you know,
• 25:29
  you're you're glowing and machine learning infrastructure.
• 25:35
  You're right machine learning infrastructure.
• 25:36
  That In the right company that could still at that could
• 25:39
  actually get you into a data engineering team or into a
• 25:42
  machine learning engineer role. So it's about how you can relate
• 25:46
  that to what they're doing. And I'm most cases, even though
• 25:51
  I actually have company saying that they want, they want, they
• 25:57
  machine learning engineer's to have software engineering
• 26:00
  backgrounds. But then, it's also the case,
• 26:02
  when you get a software engineer, they have to be
• 26:05
  committed to maybe going back and looking at the sea.
• 26:07
  Evie and saying, okay, I need to change this to make it more data
• 26:10
  focused. And with that, I just, as I
• 26:12
  said, it's just about talking about how you were deploying
• 26:15
  code, talking about what you do in software, that is very
• 26:18
  relatable to what you could do in data.
• 26:22
  Hmm. Yeah, that's really insightful.
• 26:26
  And just before we get rolling I see William has a question here
• 26:30
  on thoughts about publications when they don't directly apply
• 26:33
  to the job. She put them in.
• 26:35
  Should you not? Yeah so I would always I would
• 26:38
  always include Publications just for me I suppose.
• 26:42
  This is this is personal and this is off my my own.
• 26:46
  Am my own experience, I find that they Did they hold less way
• 26:53
  if you're going to review CV, they hold this way.
• 26:56
  But if you have a publication on recommendation system or how to,
• 27:00
  you know, it's something super specific.
• 27:02
  Then in some ways, I would make a personalized CV for one
• 27:05
  company. You know, if you do something in
• 27:07
  the area of geo-informatics or geospatial and you're applying
• 27:12
  to, I think the company that does 42 and or re Technologies
• 27:20
  or one of these companies, then yes, it holds more value with
• 27:23
  them. So it's kind of 12 gauge for I
• 27:30
  find it if if you've done a PhD and if you got Publications that
• 27:33
  are not relevant to the company that you're applying to, they do
• 27:36
  what they do, hold less way, it's definitely going to come up
• 27:39
  in the interview process but just in terms of getting over
• 27:43
  the initial hurdle of getting an interview.
• 27:46
  It's as I said, the way is more so in role description or a
• 27:51
  description of yourself. Very cool.
• 27:56
  And so now, you have an example. What is this example of?
• 27:59
  I have an example, I have an example.
• 28:01
  So one is from a data. Warehouse engineer is working in
• 28:05
  zalando for a year and a half. And this is what he has shared.
• 28:10
  And as this experience I suppose, it's just it's just
• 28:15
  pretty difficult to make so hard to work with.
• 28:18
  I suppose. It's if I had a better example,
• 28:21
  I will choose a company that isn't the Londo and, but again,
• 28:25
  I had to do this, these things on there.
• 28:28
  Had to be relatively cautious about that because everybody in
• 28:30
  Berlin knows who Salon do is there a real good company.
• 28:33
  They only hire generally hire really good people.
• 28:36
  But if you were in Portugal, the new I've never heard as a
• 28:39
  landowner and you see this, you probably wouldn't think that
• 28:42
  this guy is, is hooked up to that good.
• 28:46
  There's not a lot of information about it.
• 28:49
  Don't know if he again, he could just be an admin function of
• 28:53
  spark and poison. He could just be sitting there,
• 28:55
  you know, running IDs, anything fairly simple or anything
• 29:00
  low-level like that. I am aware of, is the comparison
• 29:04
  is I wanted to explain them so that it's a little bit
• 29:07
  different, but it's just that It's a guy who's a
• 29:10
  machine-learning engineer and I just think it was, it was a
• 29:14
  fantastic explanation of how he could talk about what he
• 29:18
  actually built and how he built it from from nothing at.
• 29:25
  So, it's an end-to-end development of how he explains
• 29:28
  what he's, what he started, what are you doing?
• 29:31
  And then how we implemented it. And you can see it, clearly
• 29:34
  distinguish what level of experience.
• 29:36
  He has won a certain technology She so he's got poison, the
• 29:39
  Django. So Django's framework for is use
• 29:42
  point in the build the entire web application for this data
• 29:47
  transcription platform, which I think is just absolutely
• 29:51
  fantastic. And I think you kind of want to
• 29:53
  be thinking how can you explain your projects like that?
• 29:58
  And it's something that I would say again about 10% of resumes
• 30:03
  and But with this you know when someone sees this and they can
• 30:08
  they can relate to that experience.
• 30:10
  They can see how that's relevant their business.
• 30:12
  You know the might not be able speech data, put taking say
• 30:16
  ought to know. We're actually we're actually
• 30:18
  looking for a full stack machine learning.
• 30:19
  Then today we're probably who can you know, deploy their own
• 30:23
  at deployed around. Doctor Uncle runs around models.
• 30:32
  Anyone has any questions on that?
• 30:33
  I think that's That's me. That's on CBS.
• 30:40
  Yeah, that is really helpful. Thank you for that.
• 30:43
  And giving a shout out right now on social media for sure if
• 30:49
  anyone wants to connect with you.
• 30:52
  Also I think that you haven't hit your limit yet on LinkedIn
• 30:56
  or have you? No no I haven't.
• 31:00
  So they can reach out to you on LinkedIn to but now you have the
• 31:04
  next part before we jump into this.
• 31:06
  Interview part. I wanted to know, I had a few
• 31:09
  questions about the resumes and CVS, does the design matter at
• 31:14
  all? Do not download your CV from
• 31:18
  LinkedIn. It's something that I say, it's
• 31:27
  it just looks like a like a complete lack of effort and I
• 31:32
  know they say searching for a job, she'll be easy.
• 31:36
  But you gotta think you're up against other people.
• 31:40
  What do you think? You're a person's resume.
• 31:41
  Looks like. So it's about beating that
• 31:44
  without faith, without sounding bad, it's about beaten or being
• 31:48
  on par with in order resume. You want to make sure that they
• 31:51
  don't just flick across yours and go to the next person.
• 31:54
  You want them to read yours and not have to go to the next
• 31:57
  person. So, and I find that I kind of go
• 32:01
  for simple. I just I don't have any sighs.
• 32:04
  I just have straight down one page size, 11 font my name
• 32:11
  introduction. All me personal information,
• 32:14
  education career, none of this side stuff with bar, charts
• 32:19
  coming across. I don't, I don't do that.
• 32:22
  That's me personally. But I think, you know, they're
• 32:26
  not necessary. They might take a little bit
• 32:28
  longer to do. I've never done one.
• 32:30
  So I don't know if they take longer but I think you know,
• 32:34
  just the old skill format and don't be afraid for them that be
• 32:37
  more than two or three pages long great.
• 32:40
  Yeah. And and so while we're talking
• 32:42
  about that and not downloading it from LinkedIn, I wanted to
• 32:46
  show a few because these days it and correct me if I'm wrong.
• 32:51
  Every time it, when do you start looking at someone's LinkedIn?
• 32:56
  Do you look at resumes resumes? Resumes and then, if there's
• 32:58
  something that Sparks Interest, you go to their LinkedIn.
• 33:01
  How does that work? Or is it linkedin's first and
• 33:04
  then resume? Or it depends?
• 33:07
  It depends on the source. The Source might be contact,
• 33:09
  someone on LinkedIn. But if you want to be getting
• 33:11
  contacted by, you know, your dream company, or you want to be
• 33:16
  contacted by a strong company who have a good talent
• 33:19
  acquisition team, you want them to contact you or maybe not but
• 33:24
  you want them to at least be looking at you at some stage and
• 33:29
  it Could LinkedIn is for that and we see some good on
• 33:31
  LinkedIn. We've never its candidates out
• 33:33
  there who we haven't spoken with him or a team.
• 33:35
  And we're like, you know, I'd love to speak with a person.
• 33:37
  A better grade and I thought that's just it.
• 33:41
  It sets a perception. So yeah.
• 33:44
  But if we have someone who comes to an advert, we will speak to
• 33:47
  them or most likely. I'll be speaking with them.
• 33:50
  I will have a look at their resume and their LinkedIn before
• 33:52
  hand and I'll be looking on LinkedIn to see if they're
• 33:55
  speaking at meetups or other events or whatever.
• 33:59
  It will be. But that's just because that's a
• 34:01
  personal thing that I look for, and as well.
• 34:04
  And are you looking at there? So, I imagine you're looking at
• 34:07
  their profile on LinkedIn. You're also looking at what
• 34:11
  they're posting and what their featured in all of that and and
• 34:15
  the same rules. Apply, I would imagine for the
• 34:17
  resume and how you describe your work experience as how you put
• 34:22
  it on LinkedIn. Yes, yeah, great great.
• 34:26
  Yeah, they can be the exact same.
• 34:28
  That's fine. Uh-huh, yeah, yeah.
• 34:32
  Cool. Good.
• 34:33
  Good, so, yeah. Now what's, what's next?
• 34:37
  What do you got in that box of tricks for us?
• 34:40
  Here we go. This one is just a little bit
• 34:44
  longer. It's there now, it's more once
• 34:50
  you get through the gates, you've been able to land an
• 34:55
  interview, how to go about that and how to make yourself shine
• 35:00
  in this interview process, right?
• 35:02
  Yes, I'm going perfect. So just if we're all kind of
• 35:09
  data guys here, I am. So I supposed to give you a
• 35:13
  little history for me, my fourth one year and a half.
• 35:18
  In recruitment. I averaged about I had to get
• 35:22
  tortie people interviews to get one deal and that means it deal
• 35:26
  is get some of the job at the end of the day and I was I was
• 35:30
  average and then I really changed my game and I said, okay
• 35:34
  look I wanna I want to maximize everyone's opportunity.
• 35:37
  I want to start getting these people at interview job offers.
• 35:40
  And then I went quickly from 30 interviews to between four and
• 35:45
  five and that was with these notes and it completely changed
• 35:49
  the way. I And we always used to have
• 35:52
  this this phrase, It's not the, it's not the best candidate who
• 35:55
  gets the job. It's the best prepared
• 35:58
  candidate. I know myself.
• 36:01
  You know, I actually really believe that and it's feedback
• 36:08
  that I get far too. Often is the kind of AD didn't
• 36:12
  do the research and how they didn't know anything about us,
• 36:16
  and I know you're still expecting modern day and you
• 36:19
  want, Companies will make the cell to you, but as I said, I'm
• 36:24
  in the game of giving people a choice, you will get for during
• 36:27
  interview processes. The more, you know, about the
• 36:30
  company. Its they want to see you want to
• 36:33
  see if they like you. But at the end of the day, they
• 36:35
  also want to see if you like them.
• 36:37
  And if you can't go in and you can't tell them anything about
• 36:40
  that company. They're not going to feel that
• 36:42
  you like them. They're not going to feel that
• 36:43
  you're interested. You're not a thought in check
• 36:47
  out the website. Check out any articles they're
• 36:49
  finding You want to write things you want to check out that
• 36:52
  LinkedIn page example. Look there's an event.
• 36:55
  All right look you're at this middle.
• 36:57
  I was actually at that made up to what did you think?
• 37:00
  Check the news tab on Google. Very simple computers.
• 37:03
  You can check out the Realms of investment.
• 37:06
  It's always better to tell companies is I see you like,
• 37:10
  he's got investment six months ago.
• 37:11
  That must be excited. You don't want them to tell you
• 37:14
  but you do want them to tell you, but if you can do it, you
• 37:17
  look good as well. Check the interviewers on
• 37:19
  LinkedIn. I all I had a can either people
• 37:23
  say to me. Oh, but I don't want them to
• 37:24
  know I've checked the morning then I'm like why you want them
• 37:27
  to know, you want them to know that you're checking them on
• 37:30
  LinkedIn two days ago, three days ago.
• 37:33
  Won't day before one hour before, that's fine to me.
• 37:37
  I actually prefer it when people do that for me as well, even
• 37:40
  when I'm speaking with people to podcast with them, it means
• 37:43
  they're taking me seriously. And I that's that's one thing
• 37:46
  that I think is great. And I share a lot of articles on
• 37:49
  LinkedIn. So articles actually have an
• 37:53
  article written about this of articles about seavey's articles
• 37:57
  about new technology, breakthroughs.
• 37:59
  That's that's all fine, other stuff that I do.
• 38:02
  So this is things I do besides my job.
• 38:05
  I also do stuff with coder dojo and I volunteer at certain
• 38:10
  organizations in our Island You want to check if this person has
• 38:15
  them. Oh, I meet with Anthony, he's a
• 38:18
  founder. And they are in action.
• 38:19
  Me of to know. I actually attended that me up.
• 38:22
  I didn't know that he was the founder.
• 38:24
  I'm going to have to say that to moment to speak with them, and
• 38:27
  then, you know, straight away. You're getting the cross, you're
• 38:30
  shown your own interests because you've done research and you're
• 38:35
  automatically start to build very good rapport with this
• 38:38
  person. Subconsciously, what they'll
• 38:41
  start to think as I call you. You know, I just met Anthony,
• 38:44
  you know, I get the feeling, I get the feeling we get on
• 38:48
  because we have so much in common, and you just need to
• 38:51
  start looking for that. The second point that I say,
• 38:56
  it's the star interview technique, this isn't new.
• 39:00
  If you research how to In and Out the pass an interview on
• 39:03
  Amazon, you will see the star technique.
• 39:06
  I have a very simple way of describing this if you pitch
• 39:10
  yourself driving a car and it's mine, Newell, you don't go from
• 39:14
  Foursquare to second gear to fifth gear, you'll ruin the
• 39:17
  engine. So if you just answered an
• 39:19
  interview by saying the start and leave out the rest.
• 39:23
  So a lot of people just say start and tasks or they even
• 39:27
  just say task and action and then the Mississippi you're
• 39:30
  ruining your interview ahem. Oh actually, thought I'd
• 39:36
  Montana. So yes.
• 39:37
  So what I say, yeah. So what I say is Give an example
• 39:42
  for everything. That you worked on.
• 39:46
  So one thing that I would owe if they if someone asked you a
• 39:49
  question, hey, have you worked on machine learning?
• 39:52
  Don't be like, yeah, I have. Yeah, two loads machine, man.
• 39:56
  And I would always think of back to a specific example or a
• 40:00
  specific challenge that you worked on.
• 40:02
  Oh yeah. And what's your experience like
• 40:04
  machine learning? You know, I've actually work as
• 40:06
  a machine learning engineer for the last five or six years,
• 40:08
  whatever it may be working with these particular Technologies on
• 40:11
  a daily basis, but At a very interesting project recently
• 40:15
  where I had to work the full stack machine learning engineer
• 40:19
  on a particular challenge. Let's say, I always go back as
• 40:23
  you can guess. Recommendation system seems to
• 40:25
  be something. I spend a lot of time talking
• 40:27
  about because I say it all the time and I worked on this
• 40:30
  recommendation engine and then you want to talk about.
• 40:33
  As I said the example that I gave previously, you know.
• 40:36
  So I worked on a recommendation engine, we use fighting which
• 40:41
  you know, a lot of people can give If you want to talk about
• 40:45
  the team, I like to know, I was part of it data, engineering
• 40:48
  data, science, cross collaboration team, those
• 40:50
  analysts. And the team you want to talk
• 40:52
  about what your input was in the team.
• 40:55
  What the each individual person was, and where user all coming
• 40:58
  in to meet, in the middle, to add to add this value.
• 41:02
  And I would say, you know, know the difference between a good
• 41:04
  good example on a bad example. So, as I said, it's situation
• 41:09
  task Action and result. So with this, you want to go
• 41:20
  into an interview, you want to look or you want to pick your
• 41:24
  core. Experience that you feel, you
• 41:28
  can you can knock it out of the park with.
• 41:31
  So, if it's, if you're a data engineer, you want to, you want
• 41:33
  to be going in expecting peyten, big data.
• 41:35
  May be streaming data, and maybe it could be a particular topic,
• 41:39
  like, Kafka you might be expecting something on a code
• 41:43
  deployments, but you can kind that you can kinda George based
• 41:46
  on a job spec, what the five Technologies would be not a
• 41:50
  popular one, is air flow. Where do you use it?
• 41:53
  So you kind of want like you want to prepare a star.
• 41:56
  Like answer for all of these and if if you feel it's seven
• 42:02
  topics, you prepare seven topics.
• 42:04
  If it says, you know what you know we're looking at a job spec
• 42:09
  what it's going to be. But if you're working with me or
• 42:11
  I would like to know that I would have these sort of
• 42:14
  questions and ready for you beforehand or what sort of
• 42:17
  angle, it's going to go down. But if you haven't spoke with
• 42:20
  me, and if you're doing this directly, have a look at the job
• 42:23
  spec, they give off a lot. Here's an example topics like
• 42:28
  this, it can be very simple and it makes all the difference and
• 42:32
  the reason why you do it is because a lot of the time you
• 42:37
  speak in what I call internal language, if you ask me, what do
• 42:41
  I do? Every day I work on bullhorn,
• 42:43
  find candidates on LinkedIn and I sent them out two monitors.
• 42:46
  That makes no sense to anybody on this video with.
• 42:49
  That's what I do every day. If I went into an interview and
• 42:52
  I said that to someone they would have no idea what I'm
• 42:55
  talking about. About.
• 42:56
  So I have to tell them what bullhorn is why our user.
• 42:59
  The reason why I use a value of me using bullhorn and that
• 43:02
  brings to the business. Same with LinkedIn.
• 43:04
  And I, same with want to send that is and you need to try and
• 43:07
  bring that into into you or into your interview game.
• 43:16
  Talk about you. So as they do and they don't
• 43:20
  they don't care about what the team does, but you need to talk
• 43:22
  about your individual contribution, an example that I
• 43:26
  would always give for this. I'll hire a manager might get
• 43:29
  very excited if they see. Oh, man.
• 43:31
  This guy just came over from the states machine learning engineer
• 43:34
  and Netflix, he's going to be great and then if everything
• 43:37
  he's talking about is all you have my team, my team.
• 43:40
  Don't this my team known that the never going to know how good
• 43:42
  this person was got to talk about your individual
• 43:45
  contribution. So, the team was worked was
• 43:48
  responsible for delivering this project.
• 43:51
  I was part of the team of Four Women was ux when was you?
• 43:53
  I won't was data analyst or I was machine learning.
• 43:58
  This is what I was. This was always supposed to
• 44:00
  deliver this is when I had to deliver it, this is the value.
• 44:03
  I was asked to bring but deliver this project but delivering this
• 44:07
  part of the software and then when I deliver this, I was
• 44:10
  expecting each other person that have delivered.
• 44:13
  This and when we did, we were able to we were able to release
• 44:17
  it over there for a bit. I know very hard one to say
• 44:20
  because each Each story is going to be in particular two to
• 44:23
  someone or a particular project. One thing is, well, I would
• 44:29
  always recommend and I actually do religiously with everyone is
• 44:35
  practice. Practicing interview.
• 44:37
  A lot of people say that. Yeah, you know what?
• 44:40
  I'm great at. I'm great at interview.
• 44:42
  I'm like, how do you know when was your last interview 18
• 44:44
  months ago? It's like okay, how are you
• 44:47
  Harry a great interview? And how do you know?
• 44:50
  Because when I interview I got the job, I was like but I
• 44:53
  guarantee you don't get every you don't get a job offer after
• 44:56
  Grand rounds. The end of every process.
• 44:59
  What it is is, you know, you go, you interview with 10, 20
• 45:02
  companies, you get two job offers and you say, you're a
• 45:05
  great interviewer, that's 10%. 10% is realistically a fail?
• 45:09
  I am Liverpool my football team and the best football team in
• 45:14
  the world right now. They practice, five days a week
• 45:17
  to play one game on the weekend. They practice.
• 45:20
  You want to be a, our video record myself or voice record
• 45:24
  myself. And then when you listen back
• 45:26
  and you hear all these Little things, you will correct
• 45:29
  yourself on the star interview technique.
• 45:32
  And you will, then start critiquing yourself of where
• 45:34
  you've gone wrong and where you could have gone better.
• 45:37
  And actually, you know what, if I wasn't me, I wouldn't
• 45:39
  understand what that answer means.
• 45:41
  And that's where I mean practice and taking into example, you
• 45:45
  know, Roger Federer, tennis or anything.
• 45:50
  Yeah, and it gives you that ability to articulate yourself
• 45:53
  better, I noticed just by doing these every week that Sure, I
• 45:57
  see. Oh, I say the same word over and
• 46:00
  over or oh, I say, um, too much maybe.
• 46:03
  And so if you're recording yourself continuously, you're
• 46:07
  going to get better and take out those unnecessary ticks that you
• 46:10
  have that can give off some nervousness or whatever it is.
• 46:16
  And so I definitely agree with that practice and we all know
• 46:19
  that you never learn anything without practice.
• 46:22
  Yes, but not many people practice interviews.
• 46:27
  And that's one thing that I that I found as my example.
• 46:32
  I did say Barcelona, Real Madrid.
• 46:33
  I think I did make these slides last year, for Liverpool, won
• 46:36
  the Champions League Quarter self and listen back.
• 46:40
  I have tried in the mirror or ask a friend colleague and
• 46:44
  partner to listen to you, and if they can understand what you
• 46:47
  mean, then, you know, you gotta write your own answers.
• 46:52
  Be harsh and I got my job in recruitment because I am I video
• 46:57
  record myself and it was something that was new to me and
• 47:02
  I don't, I don't pretty well in that having questions prepared.
• 47:06
  So, I kind of think there's two parts to this.
• 47:09
  This having questions, pre-prepared and then there's
• 47:12
  also having questions that you understand in the interview.
• 47:16
  So pre-prepared questions, you know, us the challenges.
• 47:20
  Do you face, what trends do you see in technology affected your
• 47:24
  environment? Those are kind of expected, you
• 47:28
  know, culture and how do you, how do you rate someone as a
• 47:32
  good employee? I also think that there's some
• 47:36
  questions that you can ask that we call maybe like a killer
• 47:39
  question, you know, if I was to get this job after 6 months, how
• 47:43
  would you, how would you rate me as being a successful higher?
• 47:46
  What would I have brought to the table?
• 47:48
  And then they're going to come back and say oh you know, you
• 47:51
  would have actually been able to work on this project and add
• 47:54
  this sort of value. You gives you an opportunity to
• 47:57
  go by actually, you know what, I've done that in this company,
• 47:59
  we haven't talked about it in the interview but I just want to
• 48:02
  tell you now while we're on the topic and you want to look out
• 48:06
  for the questions like that, along with what I would also say
• 48:11
  if they mention something in the interview.
• 48:15
  That you asked you want to ask a question about ask a question
• 48:18
  again shows you're listening shows better communication.
• 48:22
  They're going to make sure this guy gets me.
• 48:25
  He was answering back. He was answered.
• 48:26
  He was asking questions about what I was telling them.
• 48:29
  He was engaged. He was listening.
• 48:31
  So just two types of questions, it's very basic question,
• 48:36
  questions about the company questions about the role and
• 48:41
  then I would always say, let them know you're interested.
• 48:44
  Not many candidates doing. But again, if you want to give
• 48:48
  yourself choice, this also comes into the cultural aspect of
• 48:52
  things and they'll say, you know, someone fails of a
• 48:55
  cultural interview, I would say 80% of the time.
• 48:59
  That's because they don't think you'd like to work there or you
• 49:02
  haven't showed enough excitement about their company for them to
• 49:08
  feel like they want you to. And I know how that is and I'm I
• 49:13
  don't like, People saying they failed interviews on culture
• 49:17
  because I don't think it's fair. I am and it's very hard to judge
• 49:21
  someone in an interview. But this is where I would always
• 49:25
  say to Noah pickup points for the interview that you like the
• 49:29
  jobs back or things you've discussed.
• 49:31
  That's why I want to work at to know what, you know what Anthony
• 49:34
  I really love to work with you at all this because I seen that
• 49:37
  you do in Ai and action podcast Ai and actually meet ups you
• 49:40
  seem to share great content on me.
• 49:42
  Then you look like you're a really friendly guy.
• 49:44
  Way to work with, I think I get on well with you.
• 49:46
  I'd love to join your team. I'm leaving my this guy.
• 49:51
  He, if he join my team, he put his heart and soul into a, I
• 49:54
  want him part of my team and it's so simple and yet so
• 49:58
  effective. It's good.
• 50:01
  And then also, if they're ever between two candidates, they're
• 50:05
  gonna go with the guy, they know, like them.
• 50:07
  There's a sign-off process within all these companies if
• 50:10
  you want to say, oh, look, we want to hire X and Y.
• 50:14
  We are currently get sign off to raise one offer.
• 50:17
  They can't go all. We want to hire one guy, but
• 50:19
  we've got two candidates that might take it.
• 50:22
  Can we get sign off for? Both of them?
• 50:24
  Doesn't work like that. They're going to come in and
• 50:26
  they're going to say we like this guy.
• 50:29
  Can we get sign off for him? If you let them know if they
• 50:32
  feel, you're going to take it, that's going to be, you know,
• 50:35
  you might have said this to two or three other companies but
• 50:38
  then you're getting two or three different choices.
• 50:41
  And as I said, I'm here to try and help you get choice.
• 50:45
  I am think this might be the last part I have on this, but
• 50:48
  it's challenges or mistakes. You've made this to me is, it's
• 50:52
  kind of like a fluffy communication style question.
• 50:57
  You don't want to be the person that's never had a mistake that
• 51:02
  in their whole lives and you want to be able to tell them how
• 51:06
  you had a mistake and how you won't have that mistake again.
• 51:10
  And so there's a, there's a story I heard about a truck
• 51:14
  driver. It was truck driver who was
• 51:16
  driving like the HTV delivery trucks and he was there for like
• 51:20
  six years one day, he came in, he crashed the truck into
• 51:23
  another truck in the yard and the next day came into his boss
• 51:29
  and he handed him the case. This is actually a true story of
• 51:31
  someone, I know in Ireland and he handed his boss.
• 51:33
  The case. He said, look, I'm sorry about
• 51:35
  yesterday. Of course you a lot of money.
• 51:38
  Here's my keys. I'm gonna resilient and the boss
• 51:41
  said, no, do you think I'm gonna let you have that experience?
• 51:45
  Of having that crash known. You're never going to crash
• 51:47
  again because you're going to be extra careful.
• 51:50
  I'm going to let you give that experience to someone else after
• 51:53
  me, pain, for your accident. So it's a way it's an example
• 51:59
  that someone told me was, they tried to implement text driven
• 52:02
  development before after watching it, online webinar.
• 52:05
  And then the next code that they tried to do in test-driven
• 52:08
  development failed. But then his boss actually
• 52:11
  turned around to my office and I see you trying to test driven
• 52:14
  development. Are, you know, Oh, that didn't
• 52:15
  work but we actually have a guy who specializes in that the
• 52:18
  team, why don't you link up with him on the next project and then
• 52:21
  try and ruin it and test-driven development and then we can see
• 52:24
  where you go from that and then like it's just it's just great.
• 52:28
  It shows that you failed shows that you should, you showed you
• 52:30
  what the to improve and he showed the capability or and
• 52:33
  that you wanted the effort that you've actually made to improve
• 52:36
  or it could be something that you approach the boss about.
• 52:39
  This particular thing or technology that you wanted to
• 52:42
  improve on and yeah, real fast. I know that you interviewed
• 52:48
  Alexi right? I think that's how I may have
• 52:51
  even found you who's a predominant data scientist on
• 52:55
  LinkedIn and he shares a lot of great tips about getting jobs
• 52:59
  for data scientists and he has a GitHub repo that I'm going to
• 53:05
  put in the chat and it's just a bunch of data science interview
• 53:08
  questions. Yeah 160.
• 53:11
  Yeah. 160 different questions that and All he kind of
• 53:15
  crowdsourced the whole thing. So people have put their best
• 53:19
  answers to these questions so hopefully that can be a great
• 53:22
  resource for everybody too. But that was my last slide.
• 53:29
  I am on the, how to prepare for an interview.
• 53:34
  Awesome, awesome stuff. So if anybody has any questions,
• 53:37
  you can throw it in the chat or just go ahead and unmute
• 53:41
  yourself and ask away, feel free.
• 53:45
  I'll let I'll be quiet for 2 seconds.
• 53:48
  In case anybody wants to say anything.
• 53:52
  Okay, so I do have a question. Let me, let me get this here.
• 53:58
  Now, first off, I want to say the the idea of you telling the
• 54:04
  company that you really like them, that blew my mind.
• 54:07
  So thank you for that. That one is what you would be
• 54:11
  surprised as I am, as I say to everyone, it doesn't matter if
• 54:16
  your talent, if your interview with 10 companies, your talent
• 54:19
  10 companies, you like them because, well, of course, If you
• 54:23
  want to get, if you want to maximize your chance of getting
• 54:26
  10 offers. Yeah, particularly Rachel.
• 54:31
  Yeah yeah yeah. And so how do you feel about the
• 54:35
  LinkedIn stocking? I know you said you want people
• 54:37
  to do their homework before the interview.
• 54:41
  How do you feel about reaching out connecting with these people
• 54:44
  before you do the interview? Does that is that also something
• 54:47
  you would recommend? Yeah.
• 54:49
  And something as well that I also recommend is up in Message
• 54:53
  after an email or Ellington message, I think, hey, Noah.
• 55:00
  And you kind of want to do it within 12 hours before they've
• 55:03
  made a decision. So you kind of want to share
• 55:06
  with them your feedback on the interview.
• 55:08
  But you know what, I really like this particular aspect again,
• 55:11
  you're telling them you're interested.
• 55:12
  What you liked? This was actually really
• 55:14
  interesting opportunity. I like the way you guys are
• 55:16
  working with X Technologies on serve and why customers or
• 55:20
  users? I think I would I would really
• 55:22
  We enjoy working on these challenges, you know, and it's
• 55:28
  it just creates open to the world between, you know, this
• 55:31
  person and again, it's this guy just gets me.
• 55:34
  We're on the same level, he what he wants to work on and solve
• 55:37
  challenges, we're already solvent, he's gonna enjoy
• 55:39
  working here. Wow.
• 55:43
  Yeah, that is perfect. So unless there's any other
• 55:46
  questions from the crew that we have here with us today?
• 55:50
  I'm gonna plug what you're doing Anthony.
• 55:53
  If anyone is looking for a job in Berlin or Ireland for that
• 55:58
  matter or NYC, you guys are also there reach out to Anthony or
• 56:02
  anybody at all this. I would imagine they can help
• 56:05
  you out and also check out your podcast to there's great
• 56:09
  resources being shared their, you have the Meet up that you're
• 56:14
  doing. I imagine you're not meeting up
• 56:15
  in person. Anytime self actual virtual meet
• 56:19
  up, that is another great resource to have, but it's been
• 56:23
  a pleasure talking with you really.
• 56:25
  Thank you so much for coming here and sharing this with all
• 56:28
  of us. I know I've been able to learn a
• 56:31
  ton from it and I'm not even in the data science field.
• 56:34
  So I would imagine that everyone else has got it.
• 56:37
  I know, William asked in the chat beforehand about how to
• 56:42
  hire people. So if you're hiring manager, how
• 56:44
  do you go about that? We can try to have God.
• 56:48
  Yeah, you just he I think he just dropped off.
• 56:50
  He probably had another another meeting.
• 56:52
  But that's something that I think we talked about earlier.
• 56:56
  And we want to try and have round two of this and talk about
• 57:00
  how to hire for data science, managers or yeah.
• 57:03
  That's, that's something that I do as well.
• 57:07
  I suppose, if he's a data scientist, these are probably
• 57:11
  sick of being being I need an 8 hour test, and if you're active
• 57:15
  in the market, you might have six of them.
• 57:17
  It's no fun. Yeah, yeah.
• 57:19
  Completely, so hopefully, and if you all do get a new job, please
• 57:25
  let us know that would be like, the joy of my week or month or
• 57:30
  whenever you tell us we're in the slack.
• 57:32
  Let let Anthony, and I know because it's, it would just mean
• 57:35
  that we've been able to provide some value to you all, that
• 57:38
  would be just an awesome success story.
• 57:41
  So, I Not share the slack with you all, but I will send a
• 57:45
  follow-up email to everybody to round up the week on talk about
• 57:50
  what we learned here with some slides.
• 57:53
  Also to what Anthony was talking about and then any other little
• 57:57
  bits of wisdom that we shared. So, yeah, that's about it.
• 58:02
  Anthony, thank you so much. I really appreciate you coming.
• 58:04
  Thanks for having me. If anyone has any questions?
• 58:06
  Connect with me on LinkedIn. I have my email is Anthony at
• 58:10
  all the starcom, you'll find me on LinkedIn as Kelly Kelly, but
• 58:14
  have a great week. Everyone and stay safe and
• 58:17
  healthy. There we go.
• 58:19
  There it is. All right.
• 58:20
  See you all. Kelly Kelly, but have a great
• 58:14
  week. Everyone and stay safe and
• 58:17
  healthy. There we go.
• 58:19
  There it is. All right.
• 58:20
  See you all.